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2311.04254 | 29 | In the multi-solution scenario, the IO, CoT, and CoT-SC prompts each include 5 examples, with each problem having 1 to 3 different solutions. For ToT, the top-3 candidates (with b=3) at the final step are considered as different solutions. Rather than keeping only the top-1 thought, GoT is instructed to select between 1 to 3 thoughts from all candidates at each step to generate a wider range of solutions. As for XOT, after performing simulations on MCTS, we sample 500 thought trajectories as for exploration and remove deplicates. The top-3 thoughts with the highest counts are preserved.
4.1.3 RESULTS | 2311.04254#29 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 29,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "In the multi-solution scenario, the IO, CoT, and CoT-SC prompts each include 5 examples, with each problem having 1 to 3 different solutions. For ToT, the top-3 candidates (with b=3) at the final step are considered as different solutions. Rather than keeping only the top-1 thought, GoT is instructed to select between 1 to 3 thoughts from all candidates at each step to generate a wider range of solutions. As for XOT, after performing simulations on MCTS, we sample 500 thought trajectories as for exploration and remove deplicates. The top-3 thoughts with the highest counts are preserved.\n4.1.3 RESULTS",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} | [
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] |
2311.04254 | 30 | 4.1.3 RESULTS
Table 3 displays the overall performance of all methods on this task. Notably, XOT consistently outperforms other baselines on both GPT-3.5 and GPT-4, achieving an accuracy of 61.31% and 63.50% respectively, without revision. However, after the revision process, XOTâs accuracy sub- stantially improves to 79.56% and 74.45% for GPT-3.5 and GPT-4 respectively. This underscores the impressive performance of XOT, and demonstrates that the revision process significantly en- hances performance, with only a limited increase in the utilization of LLM and fθ. Interestingly, the revision process in XOT mitigates the performance gap attributable to the modeling ability in this task. As we observe that XOT with GPT-3.5 achieves higher accuracy after revision compared to GPT-4. | 2311.04254#30 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 30,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "4.1.3 RESULTS\nTable 3 displays the overall performance of all methods on this task. Notably, XOT consistently outperforms other baselines on both GPT-3.5 and GPT-4, achieving an accuracy of 61.31% and 63.50% respectively, without revision. However, after the revision process, XOTâs accuracy sub- stantially improves to 79.56% and 74.45% for GPT-3.5 and GPT-4 respectively. This underscores the impressive performance of XOT, and demonstrates that the revision process significantly en- hances performance, with only a limited increase in the utilization of LLM and fθ. Interestingly, the revision process in XOT mitigates the performance gap attributable to the modeling ability in this task. As we observe that XOT with GPT-3.5 achieves higher accuracy after revision compared to GPT-4.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} | [
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2311.04254 | 31 | On the other hand, the best-performing baseline, ToT (b=3) on GPT-4, attains an accuracy of 60.58%. However, it demands a substantial number of LLM invocations (39.83), which results in inefficiency. In contrast, XOT exhibits a significant advantage in terms of average LLM invocation time. It requires only a single LLM inference without revision and less than 1.4 calls with revision. Although XOT requires some inference calls for fθ, the model is significantly less complex than LLM, making it a much more efficient approach.
Table 4 presents the performance of GPT-3.5 and GPT-4 models across different methods in the multi-solution scenario. Overall, XOT remains the best-performing approach in terms of accuracy and MultiAcc, significantly outperforming other baselines. Its GPT-4 version can even achieve over
8
Table 4: Performance comparison on Game of 24 in the multi-solution scenario. | 2311.04254#31 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 31,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "On the other hand, the best-performing baseline, ToT (b=3) on GPT-4, attains an accuracy of 60.58%. However, it demands a substantial number of LLM invocations (39.83), which results in inefficiency. In contrast, XOT exhibits a significant advantage in terms of average LLM invocation time. It requires only a single LLM inference without revision and less than 1.4 calls with revision. Although XOT requires some inference calls for fθ, the model is significantly less complex than LLM, making it a much more efficient approach.\nTable 4 presents the performance of GPT-3.5 and GPT-4 models across different methods in the multi-solution scenario. Overall, XOT remains the best-performing approach in terms of accuracy and MultiAcc, significantly outperforming other baselines. Its GPT-4 version can even achieve over\n8\nTable 4: Performance comparison on Game of 24 in the multi-solution scenario.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
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2311.04254 | 32 | 8
Table 4: Performance comparison on Game of 24 in the multi-solution scenario.
Model IO CoT CoT-SC (n=10) ToT (b=3) GoT (k=3) XoT (w/o revise) XoT (w/ revise) Acc. MultiAcc 14.6 3.65 5.11 10.22 8.76 72.99 85.40 4.87 1.22 1.70 3.41 8.03 39.90 62.90 GPT-3.5 #Sol 2.88 2.77 2.76 2.99 1.93 2.89 2.29 LLM invoked 1.00 1.00 10.00 43.96 7.00 1.00 3.51 fθ invoked Acc. MultiAcc - - - - - 95.66 116.34 21.17 20.44 18.98 60.58 13.14 72.99 90.51 8.27 7.79 8.03 39.90 10.46 60.54 76.25 GPT-4 #Sol 2.99 2.94 2.99 2.78 1.39 2.55 2.36 LLM invoked 1.00 1.00 10.00 39.83 7.00 1.00 2.31 fθ invoked - - - - - 95.66 109.64 | 2311.04254#32 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
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"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "8\nTable 4: Performance comparison on Game of 24 in the multi-solution scenario.\nModel IO CoT CoT-SC (n=10) ToT (b=3) GoT (k=3) XoT (w/o revise) XoT (w/ revise) Acc. MultiAcc 14.6 3.65 5.11 10.22 8.76 72.99 85.40 4.87 1.22 1.70 3.41 8.03 39.90 62.90 GPT-3.5 #Sol 2.88 2.77 2.76 2.99 1.93 2.89 2.29 LLM invoked 1.00 1.00 10.00 43.96 7.00 1.00 3.51 fθ invoked Acc. MultiAcc - - - - - 95.66 116.34 21.17 20.44 18.98 60.58 13.14 72.99 90.51 8.27 7.79 8.03 39.90 10.46 60.54 76.25 GPT-4 #Sol 2.99 2.94 2.99 2.78 1.39 2.55 2.36 LLM invoked 1.00 1.00 10.00 39.83 7.00 1.00 2.31 fθ invoked - - - - - 95.66 109.64",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
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] |
2311.04254 | 33 | 90% accuracy. Although XOT does not generate the most number of answers compared to other baselines, it generates more accurate answers, as its MultiAcc significantly outperforms other ap- proaches. Notably, generating multiple solutions does not significantly increase XOTâs complexity, as it only requires 2.31 LLM calls with GPT-4 and around 100 calls for a smaller fθ, making it remain efficient. Overall, the remarkable performance of XOT in the multi-solution scenario demonstrates its ability to generate complex thoughts, making it a flexible approach.
4.2 8-PUZZLE
The 8-Puzzle is a classic sliding puzzle game that consists of a 3 Ã 3 grid with eight numbered tiles and one empty space denoted as â-â. Its objective is to rearrange the tiles from a given initial configuration into a target configuration. The maximum number of steps necessary for the optimal solution of the 8-Puzzle is 31. This problem falls within the category of NP-complete problems Ratner & Warmuth (1986) and may have multiple solutions.
4.2.1 TASK SETUP | 2311.04254#33 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 33,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "90% accuracy. Although XOT does not generate the most number of answers compared to other baselines, it generates more accurate answers, as its MultiAcc significantly outperforms other ap- proaches. Notably, generating multiple solutions does not significantly increase XOTâs complexity, as it only requires 2.31 LLM calls with GPT-4 and around 100 calls for a smaller fθ, making it remain efficient. Overall, the remarkable performance of XOT in the multi-solution scenario demonstrates its ability to generate complex thoughts, making it a flexible approach.\n4.2 8-PUZZLE\nThe 8-Puzzle is a classic sliding puzzle game that consists of a 3 à 3 grid with eight numbered tiles and one empty space denoted as â-â. Its objective is to rearrange the tiles from a given initial configuration into a target configuration. The maximum number of steps necessary for the optimal solution of the 8-Puzzle is 31. This problem falls within the category of NP-complete problems Ratner & Warmuth (1986) and may have multiple solutions.\n4.2.1 TASK SETUP",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
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] |
2311.04254 | 34 | 4.2.1 TASK SETUP
We randomly generated 419 solvable 8-puzzle problems, with 300 instances allocated for training and 119 instances for testing. All generated problems are solvable within 9 steps. The action space encompasses four directions: [Up, Down, Left, Right]. Note that the legal action space for each problem state may vary due to the dynamic position of the empty space. As shown in Table 1, the thoughts refer to the step-by-step move, and the puzzle state after the move.
4.2.2 BASELINES & XOT SETUP
The IO prompt is extended with three in-context examples. In the CoT approach, each input-output pair is enriched by incorporating intermediate legal action sets, the current action, and the current state. In ToT, at each stage, a set of one-step thought candidates are derived from the LLM, from the current set of legal actions. We impose a maximum step limit of 9 since all generated problems can be solved within this range. The 8-puzzleâs rules are conveyed through a system message, including detailed explanations of each actionâs execution. Similarly, we perform 20 simulations for each action taken with XOT, and increase this number to 50 for thought revision processes. | 2311.04254#34 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 34,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "4.2.1 TASK SETUP\nWe randomly generated 419 solvable 8-puzzle problems, with 300 instances allocated for training and 119 instances for testing. All generated problems are solvable within 9 steps. The action space encompasses four directions: [Up, Down, Left, Right]. Note that the legal action space for each problem state may vary due to the dynamic position of the empty space. As shown in Table 1, the thoughts refer to the step-by-step move, and the puzzle state after the move.\n4.2.2 BASELINES & XOT SETUP\nThe IO prompt is extended with three in-context examples. In the CoT approach, each input-output pair is enriched by incorporating intermediate legal action sets, the current action, and the current state. In ToT, at each stage, a set of one-step thought candidates are derived from the LLM, from the current set of legal actions. We impose a maximum step limit of 9 since all generated problems can be solved within this range. The 8-puzzleâs rules are conveyed through a system message, including detailed explanations of each actionâs execution. Similarly, we perform 20 simulations for each action taken with XOT, and increase this number to 50 for thought revision processes.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
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2311.04254 | 35 | In the multi-solution scenario, all of the IO, CoT, and CoT-SC prompts consist of four examples. Each problem is presented with one to three distinct solutions. For ToT (b=3) and GoT (k=3), the maximum number of steps is increased to 12, as correct solutions may not always be optimal and could exceed 9 steps. In the case of XOT, after conducting simulations with MCTS, we sample 50 thought trajectories for exploration and select the top-3 thoughts with the highest counts.
4.2.3 RESULTS
The inherent spatial complexity of the 8-Puzzle, the need for long-term planning, and the presence of invalid actions create a significant challenge for LLMs, which rely solely on textual data as input. This challenge is starkly evident in the poor performance of the baselines on both GPT-3.5, where its IO prompting achieve a mere 0% success rate. XOT successfully addresses this issue by supplying thoughts acquired from MCTS, thereby infusing external knowledge into the problem-solving pro- cess. This augmentation empowers LLMs to tackle problems that were previously insurmountable. In summary, when using GPT-4, XOT achieves an accuracy of 50.42% without revision and 93.2%
9
# Table 5: Performance comparison on 8-Puzzle. | 2311.04254#35 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 35,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "In the multi-solution scenario, all of the IO, CoT, and CoT-SC prompts consist of four examples. Each problem is presented with one to three distinct solutions. For ToT (b=3) and GoT (k=3), the maximum number of steps is increased to 12, as correct solutions may not always be optimal and could exceed 9 steps. In the case of XOT, after conducting simulations with MCTS, we sample 50 thought trajectories for exploration and select the top-3 thoughts with the highest counts.\n4.2.3 RESULTS\nThe inherent spatial complexity of the 8-Puzzle, the need for long-term planning, and the presence of invalid actions create a significant challenge for LLMs, which rely solely on textual data as input. This challenge is starkly evident in the poor performance of the baselines on both GPT-3.5, where its IO prompting achieve a mere 0% success rate. XOT successfully addresses this issue by supplying thoughts acquired from MCTS, thereby infusing external knowledge into the problem-solving pro- cess. This augmentation empowers LLMs to tackle problems that were previously insurmountable. In summary, when using GPT-4, XOT achieves an accuracy of 50.42% without revision and 93.2%\n9\n# Table 5: Performance comparison on 8-Puzzle.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
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] |
2311.04254 | 36 | 9
# Table 5: Performance comparison on 8-Puzzle.
Model IO CoT CoT-SC (n=10) ToT (b=1) ToT (b=3) GoT (k=1) XoT (w/o revise) XoT (w/ revise) GPT-3.5 Acc. [%] LLM invoked 0.00 0.00 0.84 5.88 6.72 3.36 49.58 59.66 1.00 1.00 10.00 31.76 55.86 19.00 1.00 1.50 GPT-4 fθ invoked Acc. [%] LLM invoked - - - - - - 36.64 41.09 1.68 7.56 8.40 3.36 13.45 3.36 51.26 93.28 1.00 1.00 10.00 27.49 54.13 19.00 1.00 1.48 fθ invoked - - - - - - 36.25 55.66
Table 6: Performance comparison on 8-Puzzle in the multi-solution scenario. | 2311.04254#36 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 36,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "9\n# Table 5: Performance comparison on 8-Puzzle.\nModel IO CoT CoT-SC (n=10) ToT (b=1) ToT (b=3) GoT (k=1) XoT (w/o revise) XoT (w/ revise) GPT-3.5 Acc. [%] LLM invoked 0.00 0.00 0.84 5.88 6.72 3.36 49.58 59.66 1.00 1.00 10.00 31.76 55.86 19.00 1.00 1.50 GPT-4 fθ invoked Acc. [%] LLM invoked - - - - - - 36.64 41.09 1.68 7.56 8.40 3.36 13.45 3.36 51.26 93.28 1.00 1.00 10.00 27.49 54.13 19.00 1.00 1.48 fθ invoked - - - - - - 36.25 55.66\nTable 6: Performance comparison on 8-Puzzle in the multi-solution scenario.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
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2311.04254 | 37 | Table 6: Performance comparison on 8-Puzzle in the multi-solution scenario.
Model IO CoT CoT-SC (n=10) ToT (b=3) GoT (k=3) XoT (w/o revise) XoT (w/ revise) Acc. MultiAcc 0.00 2.52 2.52 6.72 6.72 36.97 52.10 0.00 1.43 1.54 2.52 3.36 21.15 27.45 GPT-3.5 #Sol 2.47 2.05 1.90 2.98 2.96 2.87 2.85 LLM invoked 1.00 1.00 10.00 55.86 24.18 1.00 4.19 fθ invoked - - - - - 36.25 52.06 Acc. MultiAcc 2.52 10.92 11.76 13.45 20.17 50.42 82.35 0.84 7.84 6.58 5.60 16.61 29.13 76.33 GPT-4 #Sol 2.97 1.21 2.08 2.97 2.70 2.97 1.52 LLM invoked 1.00 1.00 10.00 54.13 22.76 1.00 4.30 fθ invoked - - - - - 36.25 66.66 | 2311.04254#37 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 37,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "Table 6: Performance comparison on 8-Puzzle in the multi-solution scenario.\nModel IO CoT CoT-SC (n=10) ToT (b=3) GoT (k=3) XoT (w/o revise) XoT (w/ revise) Acc. MultiAcc 0.00 2.52 2.52 6.72 6.72 36.97 52.10 0.00 1.43 1.54 2.52 3.36 21.15 27.45 GPT-3.5 #Sol 2.47 2.05 1.90 2.98 2.96 2.87 2.85 LLM invoked 1.00 1.00 10.00 55.86 24.18 1.00 4.19 fθ invoked - - - - - 36.25 52.06 Acc. MultiAcc 2.52 10.92 11.76 13.45 20.17 50.42 82.35 0.84 7.84 6.58 5.60 16.61 29.13 76.33 GPT-4 #Sol 2.97 1.21 2.08 2.97 2.70 2.97 1.52 LLM invoked 1.00 1.00 10.00 54.13 22.76 1.00 4.30 fθ invoked - - - - - 36.25 66.66",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
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] |
2311.04254 | 38 | with revision in the 8-Puzzle task, outperforming the best baseline, ToT (b=3), which only achieves 13.45% accuracy. Additionally, XOT demonstrates efficiency, requiring approximately 1.5 LLM calls and around 55 calls to fθ, while delivering significantly superior performance.
The multi-solution performance presented in Table 6 confirms that the XOT method continues to outperform other baselines for both GPT-3.5 and GPT-4 models in terms of accuracy and MultiAcc, whether or not revision is applied. Itâs worth noting that the revision process is particularly beneficial for GPT-4, as it improves the MultiAcc from 29.13% to 76.33%. These results once again demon- strate that XOT can effectively generate complex thought structures for complete multi-solutions with high performance and efficiency, making it particularly suitable for this task.
4.3 POCKET CUBE
The 2 Ã 2 Pocket Cube is a simplified variant of the classic Rubikâs Cube puzzle. Its primary objec- tive is to restore all of its faces to a uniform color by executing various face rotations. The maximum number of steps required to optimally solve the cube is 11, and it is also a NP-complete problem Demaine et al. (2017) and may possess multiple solutions. This task is known to be challenging to LLMs cub. | 2311.04254#38 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 38,
"doc_id": "2311.04254",
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"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "with revision in the 8-Puzzle task, outperforming the best baseline, ToT (b=3), which only achieves 13.45% accuracy. Additionally, XOT demonstrates efficiency, requiring approximately 1.5 LLM calls and around 55 calls to fθ, while delivering significantly superior performance.\nThe multi-solution performance presented in Table 6 confirms that the XOT method continues to outperform other baselines for both GPT-3.5 and GPT-4 models in terms of accuracy and MultiAcc, whether or not revision is applied. Itâs worth noting that the revision process is particularly beneficial for GPT-4, as it improves the MultiAcc from 29.13% to 76.33%. These results once again demon- strate that XOT can effectively generate complex thought structures for complete multi-solutions with high performance and efficiency, making it particularly suitable for this task.\n4.3 POCKET CUBE\nThe 2 à 2 Pocket Cube is a simplified variant of the classic Rubikâs Cube puzzle. Its primary objec- tive is to restore all of its faces to a uniform color by executing various face rotations. The maximum number of steps required to optimally solve the cube is 11, and it is also a NP-complete problem Demaine et al. (2017) and may possess multiple solutions. This task is known to be challenging to LLMs cub.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
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] |
2311.04254 | 39 | 4.3.1 TASK SETUP
We initially set all faces of the cube to a uniform color and then randomly apply 5 actions sequen- tially selected from the 27 legal actions of the Rubikâs Cube. This process resulted in the creation of 1,000 training samples and 183 testing samples. All generated problems can be solved within 4 steps. To simplify the action space, we reduced the 27 legal operations to 9 actions, namely: {U, Uâ, U2, R, Râ, R2, F, Fâ, F2}, which are used in our experiments with both baselines and XOT. As shown in Table 1, the thoughts pertain to the step-by-step rotation, and the cube state after the move.
4.3.2 BASELINES & XOT SETUP
The IO prompt is augmented with a single in-context example. In CoT, we enrich each input-output pair by including intermediate actions and states. In ToT, we retrieve one-step thought candidates from the LLM at each stage and instruct the LLM to classify each candidate for intermediate selec- tion. A maximum step limit of 4 is imposed, as all generated problems can be resolved within this range. The cubeâs rules are conveyed through a system message, which includes the definition of the
10
Table 7: Performance comparison on Pocket Cube. | 2311.04254#39 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 39,
"doc_id": "2311.04254",
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"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "4.3.1 TASK SETUP\nWe initially set all faces of the cube to a uniform color and then randomly apply 5 actions sequen- tially selected from the 27 legal actions of the Rubikâs Cube. This process resulted in the creation of 1,000 training samples and 183 testing samples. All generated problems can be solved within 4 steps. To simplify the action space, we reduced the 27 legal operations to 9 actions, namely: {U, Uâ, U2, R, Râ, R2, F, Fâ, F2}, which are used in our experiments with both baselines and XOT. As shown in Table 1, the thoughts pertain to the step-by-step rotation, and the cube state after the move.\n4.3.2 BASELINES & XOT SETUP\nThe IO prompt is augmented with a single in-context example. In CoT, we enrich each input-output pair by including intermediate actions and states. In ToT, we retrieve one-step thought candidates from the LLM at each stage and instruct the LLM to classify each candidate for intermediate selec- tion. A maximum step limit of 4 is imposed, as all generated problems can be resolved within this range. The cubeâs rules are conveyed through a system message, which includes the definition of the\n10\nTable 7: Performance comparison on Pocket Cube.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
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] |
2311.04254 | 40 | 10
Table 7: Performance comparison on Pocket Cube.
Model IO CoT CoT-SC (n=10) ToT (b=1) ToT (b=3) GoT (k=1) XoT (w/o revise) XoT (w/ revise) GPT-3.5 Acc. [%] LLM invoked 1.09 0.00 0.00 7.65 17.49 1.64 45.36 74.32 1.00 1.00 10.00 16.50 58.72 8.93 1.00 1.55 GPT-4 fθ invoked Acc. [%] LLM invoked - - - - - - 18.69 64.63 1.09 1.09 1.09 11.48 19.57 18.03 45.90 77.60 1.00 1.00 10.00 16.39 56.58 8.55 1.00 1.54 fθ invoked - - - - - - 18.86 75.51
Table 8: Performance comparison on Pocket Cube in the multi-solution scenario. | 2311.04254#40 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 40,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "10\nTable 7: Performance comparison on Pocket Cube.\nModel IO CoT CoT-SC (n=10) ToT (b=1) ToT (b=3) GoT (k=1) XoT (w/o revise) XoT (w/ revise) GPT-3.5 Acc. [%] LLM invoked 1.09 0.00 0.00 7.65 17.49 1.64 45.36 74.32 1.00 1.00 10.00 16.50 58.72 8.93 1.00 1.55 GPT-4 fθ invoked Acc. [%] LLM invoked - - - - - - 18.69 64.63 1.09 1.09 1.09 11.48 19.57 18.03 45.90 77.60 1.00 1.00 10.00 16.39 56.58 8.55 1.00 1.54 fθ invoked - - - - - - 18.86 75.51\nTable 8: Performance comparison on Pocket Cube in the multi-solution scenario.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
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2311.04254 | 41 | Table 8: Performance comparison on Pocket Cube in the multi-solution scenario.
Model IO CoT CoT-SC (n=10) ToT (b=3) GoT (k=3) XoT (w/o revise) XoT (w/ revise) Acc. MultiAcc 0.55 0.55 0.55 17.49 3.28 39.89 73.22 0.27 0.55 0.18 5.83 1.09 23.04 48.72 GPT-3.5 #Sol 2.00 1.05 2.90 2.99 2.99 2.68 2.20 LLM invoked 1.00 1.00 10.00 58.72 14.76 1.00 4.13 fθ invoked Acc. MultiAcc - - - - - 18.95 115.73 2.19 1.64 1.63 19.57 30.50 47.54 91.26 1.09 0.82 0.82 6.52 16.85 31.97 77.41 GPT-4 #Sol 1.98 1.91 2.92 2.99 2.77 2.62 1.72 LLM invoked 1.00 1.00 1.00 56.58 13.36 1.00 4.08 fθ invoked - - - - - 18.95 122.54 | 2311.04254#41 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 41,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "Table 8: Performance comparison on Pocket Cube in the multi-solution scenario.\nModel IO CoT CoT-SC (n=10) ToT (b=3) GoT (k=3) XoT (w/o revise) XoT (w/ revise) Acc. MultiAcc 0.55 0.55 0.55 17.49 3.28 39.89 73.22 0.27 0.55 0.18 5.83 1.09 23.04 48.72 GPT-3.5 #Sol 2.00 1.05 2.90 2.99 2.99 2.68 2.20 LLM invoked 1.00 1.00 10.00 58.72 14.76 1.00 4.13 fθ invoked Acc. MultiAcc - - - - - 18.95 115.73 2.19 1.64 1.63 19.57 30.50 47.54 91.26 1.09 0.82 0.82 6.52 16.85 31.97 77.41 GPT-4 #Sol 1.98 1.91 2.92 2.99 2.77 2.62 1.72 LLM invoked 1.00 1.00 1.00 56.58 13.36 1.00 4.08 fθ invoked - - - - - 18.95 122.54",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
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2311.04254 | 42 | action space and illustrations of the execution of each action. For XOT, we conduct 20 simulations for each action taken and increase it to 500 for revision.
In the multi-solution setup, the IO, CoT, and CoT-SC prompts each include 3 examples, and each problem within these prompts offers 3 unique solutions. As for ToT (b=3) and GoT (k=3), the maximum number of steps allowed is extended to 7. In the case of XOT, after conducting MCTS simulations, we gather 50 thought trajectories, and we keep the top 3 thoughts with the highest counts.
4.3.3 RESULTS | 2311.04254#42 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 42,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "action space and illustrations of the execution of each action. For XOT, we conduct 20 simulations for each action taken and increase it to 500 for revision.\nIn the multi-solution setup, the IO, CoT, and CoT-SC prompts each include 3 examples, and each problem within these prompts offers 3 unique solutions. As for ToT (b=3) and GoT (k=3), the maximum number of steps allowed is extended to 7. In the case of XOT, after conducting MCTS simulations, we gather 50 thought trajectories, and we keep the top 3 thoughts with the highest counts.\n4.3.3 RESULTS",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
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2311.04254 | 43 | 4.3.3 RESULTS
The Pocket Cube task, similar to the 8-Puzzle, poses a challenge that demands spatial imagination skills, making it difficult for LLMs to excel. As expected, most of the baselines show very poor performance in this task, with some baselines achieving 0% accuracy. The best-performing base- line, ToT (b=3) with GPT-4, only attains a success rate of 19.57%. In contrast, XOT can achieve over 45% accuracy without revision and over 75% accuracy with revision, establishing itself as an expert in solving this task. This success is attributed to the injection of external knowledge from MCTS, enabling LLMs to solve problems that they would struggle with on their own. Notably, XOT maintains high efficiency in this task, requiring only 1.55 and 1.54 LLM inference calls for GPT-3.5 and GPT-4, respectively. These results position XOT as a superior solution for enhancing LLMs in addressing seemingly insurmountable tasks. | 2311.04254#43 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 43,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "4.3.3 RESULTS\nThe Pocket Cube task, similar to the 8-Puzzle, poses a challenge that demands spatial imagination skills, making it difficult for LLMs to excel. As expected, most of the baselines show very poor performance in this task, with some baselines achieving 0% accuracy. The best-performing base- line, ToT (b=3) with GPT-4, only attains a success rate of 19.57%. In contrast, XOT can achieve over 45% accuracy without revision and over 75% accuracy with revision, establishing itself as an expert in solving this task. This success is attributed to the injection of external knowledge from MCTS, enabling LLMs to solve problems that they would struggle with on their own. Notably, XOT maintains high efficiency in this task, requiring only 1.55 and 1.54 LLM inference calls for GPT-3.5 and GPT-4, respectively. These results position XOT as a superior solution for enhancing LLMs in addressing seemingly insurmountable tasks.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
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] |
2311.04254 | 44 | In the case of the multi-solution scenario, the performance of the XOT method remains remarkable, achieving over 91% accuracy and over 77% MultiAcc with GPT-4. The revision process continues to play an important role, significantly improving the performance of XOT with both GPT models. The closest competitor in this setting is GoT (k=3) with GPT-4, which achieves an accuracy of 30.50% and a MultiAcc of 16.85%, but it requires a significantly higher number of LLM invocations compared to XOT (13.36 vs. 4.08). Overall, XOT retains its position as the best solution for the Pocket Cube task, exhibiting high performance, efficiency, and flexibility.
4.4 ABLATION STUDY
In our ablation study, we consider two aspects: the impact of the number of revisions on the perfor- mance and efficiency of XOT and the sensitivity of performance to the completeness of the provided thoughts. These angles allow us to gain insights into how XOTâs performance can be improved and understand the importance of providing complete thoughts in complex problem-solving tasks.
11
(a) Game of 24 (b) 8-Puzzle
# (c) Pocket Cube
Figure 4: Accuracy, LLM and fθ invoked comparison on XOT w.r.t. the number of revisions.
4.4.1 NUMBER OF REVISIONS | 2311.04254#44 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 44,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "In the case of the multi-solution scenario, the performance of the XOT method remains remarkable, achieving over 91% accuracy and over 77% MultiAcc with GPT-4. The revision process continues to play an important role, significantly improving the performance of XOT with both GPT models. The closest competitor in this setting is GoT (k=3) with GPT-4, which achieves an accuracy of 30.50% and a MultiAcc of 16.85%, but it requires a significantly higher number of LLM invocations compared to XOT (13.36 vs. 4.08). Overall, XOT retains its position as the best solution for the Pocket Cube task, exhibiting high performance, efficiency, and flexibility.\n4.4 ABLATION STUDY\nIn our ablation study, we consider two aspects: the impact of the number of revisions on the perfor- mance and efficiency of XOT and the sensitivity of performance to the completeness of the provided thoughts. These angles allow us to gain insights into how XOTâs performance can be improved and understand the importance of providing complete thoughts in complex problem-solving tasks.\n11\n(a) Game of 24 (b) 8-Puzzle \n# (c) Pocket Cube\nFigure 4: Accuracy, LLM and fθ invoked comparison on XOT w.r.t. the number of revisions.\n4.4.1 NUMBER OF REVISIONS",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
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2311.04254 | 45 | Figure 4: Accuracy, LLM and fθ invoked comparison on XOT w.r.t. the number of revisions.
4.4.1 NUMBER OF REVISIONS
Itâs important to highlight that the performance of each task can be further improved through multi- ple revisions of the thought using the MCTS-LLM collaborative framework. In Fig. 4, we compare the performance of GPT-3.5 and GPT-4 models using the XOT method with varying numbers of revisions, ranging from 0 to 3, across all three tasks. | 2311.04254#45 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 45,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "Figure 4: Accuracy, LLM and fθ invoked comparison on XOT w.r.t. the number of revisions.\n4.4.1 NUMBER OF REVISIONS\nItâs important to highlight that the performance of each task can be further improved through multi- ple revisions of the thought using the MCTS-LLM collaborative framework. In Fig. 4, we compare the performance of GPT-3.5 and GPT-4 models using the XOT method with varying numbers of revisions, ranging from 0 to 3, across all three tasks.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
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2311.04254 | 46 | In the Game of 24 task, as the number of revisions increases, both models exhibit improved per- formance. Notably, GPT-3.5 consistently outperforms GPT-4 in terms of accuracy. After three revisions, GPT-3.5 achieves an accuracy of 90.51%, while GPT-4 reaches 85.40%. This improved performance comes at the cost of increased inference times and model calls, primarily driven by the need for more interactions to generate revised thoughts. For the 8-Puzzle task, the trend of in- creasing accuracy with more revisions remains valid. However, in this task, GPT-4 significantly outperforms GPT-3.5. After one revision, GPT-4 achieves an accuracy of 93.28%, which increases to 95.8% after the third revision. In contrast, GPT-3.5 only attains an accuracy of 63.03% after the third revision. In the Pocket Cube task, the performance trend is similar. The accuracy of both mod- els improves with an increase in the number of revisions. GPT-3.5 starts at an accuracy of 45.36% without revision and improves to 84.70% after three revisions. GPT-4 | 2311.04254#46 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 46,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "In the Game of 24 task, as the number of revisions increases, both models exhibit improved per- formance. Notably, GPT-3.5 consistently outperforms GPT-4 in terms of accuracy. After three revisions, GPT-3.5 achieves an accuracy of 90.51%, while GPT-4 reaches 85.40%. This improved performance comes at the cost of increased inference times and model calls, primarily driven by the need for more interactions to generate revised thoughts. For the 8-Puzzle task, the trend of in- creasing accuracy with more revisions remains valid. However, in this task, GPT-4 significantly outperforms GPT-3.5. After one revision, GPT-4 achieves an accuracy of 93.28%, which increases to 95.8% after the third revision. In contrast, GPT-3.5 only attains an accuracy of 63.03% after the third revision. In the Pocket Cube task, the performance trend is similar. The accuracy of both mod- els improves with an increase in the number of revisions. GPT-3.5 starts at an accuracy of 45.36% without revision and improves to 84.70% after three revisions. GPT-4",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
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2311.04254 | 48 | Note that the number of LLM invocations does not increase dramatically with additional revisions, even though fθ is called more times to guide simulations. Considering the significant disparity in in- ference costs between LLM and fθ, increasing the number of revisions to achieve better performance appears to be a favorable trade-off.
12
# Table 9: Performance comparison on three tasks with incomplete thoughts. | 2311.04254#48 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 48,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "Note that the number of LLM invocations does not increase dramatically with additional revisions, even though fθ is called more times to guide simulations. Considering the significant disparity in in- ference costs between LLM and fθ, increasing the number of revisions to achieve better performance appears to be a favorable trade-off.\n12\n# Table 9: Performance comparison on three tasks with incomplete thoughts.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} | [
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2311.04254 | 49 | 12
# Table 9: Performance comparison on three tasks with incomplete thoughts.
Task Game of 24 8-Puzzle Pocket Cube Model ToT (b=1) GoT (k=1) XoT (w/o revise) ToT (b=1) GoT (k=1) XoT (w/o revise) ToT (b=1) GoT (k=1) XoT (w/o revise) GPT-3.5 Acc. [%] LLM invoked 3.65 2.19 17.52 0.00 0.00 2.52 0.55 0.00 5.46 17.15 5.00 1.00 32.60 18.63 1.00 16.48 8.96 1.00 GPT-4 fθ invoked Acc. [%] LLM invoked - - 68.73 - - 36.66 - - 18.85 40.88 9.49 43.07 6.72 3.36 40.34 2.19 1.64 6.01 18.55 5.00 1.00 26.98 19.00 1.00 16.39 8.68 1.00 fθ invoked - - 68.70 - - 36.24 - - 18.89 | 2311.04254#49 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 49,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "12\n# Table 9: Performance comparison on three tasks with incomplete thoughts.\nTask Game of 24 8-Puzzle Pocket Cube Model ToT (b=1) GoT (k=1) XoT (w/o revise) ToT (b=1) GoT (k=1) XoT (w/o revise) ToT (b=1) GoT (k=1) XoT (w/o revise) GPT-3.5 Acc. [%] LLM invoked 3.65 2.19 17.52 0.00 0.00 2.52 0.55 0.00 5.46 17.15 5.00 1.00 32.60 18.63 1.00 16.48 8.96 1.00 GPT-4 fθ invoked Acc. [%] LLM invoked - - 68.73 - - 36.66 - - 18.85 40.88 9.49 43.07 6.72 3.36 40.34 2.19 1.64 6.01 18.55 5.00 1.00 26.98 19.00 1.00 16.39 8.68 1.00 fθ invoked - - 68.70 - - 36.24 - - 18.89",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} | [
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] |
2311.04254 | 50 | Game of 24 8-Puzzle Pocket Cube Initial State Initial State Initial State igh Left les a) ⬠S J5 x G+@x7)41 PB ax3)+8x7) CIS) ee EI yy 4) [y XV Left G+GxIx1 Nera | yw 67/8) Final State Final State Final State
Figure 5: Examples of thought structures generated by XOT for all three tasks in the multi-solution scenario.
4.4.2 INCOMPLETE THOUGHT
In this ablation study, we explore the performance of LLMs when provided with incomplete thoughts, specifically omitting the last step of the thought trajectory. This simulates scenarios where MCTS might supply inaccurate or incomplete thoughts. The aim is to test whether LLMs can inde- pendently solve problems or rely on their own reasoning, rather than solely relying on the thought from MCTS as answers. We present the performance comparison for all three tasks in Table 9. Note that we only compare ToT and GoT since other baselines do not support this comparison by their nature. | 2311.04254#50 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 50,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "Game of 24 8-Puzzle Pocket Cube Initial State Initial State Initial State igh Left les a) ⬠S J5 x G+@x7)41 PB ax3)+8x7) CIS) ee EI yy 4) [y XV Left G+GxIx1 Nera | yw 67/8) Final State Final State Final State\nFigure 5: Examples of thought structures generated by XOT for all three tasks in the multi-solution scenario.\n4.4.2 INCOMPLETE THOUGHT\nIn this ablation study, we explore the performance of LLMs when provided with incomplete thoughts, specifically omitting the last step of the thought trajectory. This simulates scenarios where MCTS might supply inaccurate or incomplete thoughts. The aim is to test whether LLMs can inde- pendently solve problems or rely on their own reasoning, rather than solely relying on the thought from MCTS as answers. We present the performance comparison for all three tasks in Table 9. Note that we only compare ToT and GoT since other baselines do not support this comparison by their nature.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} | [
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2311.04254 | 51 | The results clearly show that incomplete thoughts lead to a significant performance drop in all three tasks. GPT-3.5 is more affected than GPT-4, with GPT-3.5 achieving 0% accuracy on several base- lines. In contrast, XOT with GPT-4 attains satisfactory performance on the Game of 24 and 8- Puzzle, achieving over 40% accuracy. However, the performance of XOT is dramatically affected in the Pocket Cube task, with accuracy dropping to 6%. This demonstrates that for very complex tasks, LLMs are highly sensitive to the completeness of the thoughts provided. Missing steps in the thought can lead to a substantial drop in performance, highlighting the importance of providing complete thoughts for such tasks.
4.5 CASE STUDY
Finally, in Fig. 5, we provide examples of thought structures generated by XOT for all three tasks in the multi-solution scenario. It is noteworthy that, owing to the multiple solutions required, the generated thoughts intertwine during intermediate steps and converge towards the final goal state. This results in a naturally woven thought structure resembling a graph, showcasing the remarkable flexibility achieved by XOT. Upon closer examination of each example, in the case of the Game of 24, there are multiple solutions to reach the goal of 24 from the initial state. XOT effectively
13 | 2311.04254#51 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 51,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "The results clearly show that incomplete thoughts lead to a significant performance drop in all three tasks. GPT-3.5 is more affected than GPT-4, with GPT-3.5 achieving 0% accuracy on several base- lines. In contrast, XOT with GPT-4 attains satisfactory performance on the Game of 24 and 8- Puzzle, achieving over 40% accuracy. However, the performance of XOT is dramatically affected in the Pocket Cube task, with accuracy dropping to 6%. This demonstrates that for very complex tasks, LLMs are highly sensitive to the completeness of the thoughts provided. Missing steps in the thought can lead to a substantial drop in performance, highlighting the importance of providing complete thoughts for such tasks.\n4.5 CASE STUDY\nFinally, in Fig. 5, we provide examples of thought structures generated by XOT for all three tasks in the multi-solution scenario. It is noteworthy that, owing to the multiple solutions required, the generated thoughts intertwine during intermediate steps and converge towards the final goal state. This results in a naturally woven thought structure resembling a graph, showcasing the remarkable flexibility achieved by XOT. Upon closer examination of each example, in the case of the Game of 24, there are multiple solutions to reach the goal of 24 from the initial state. XOT effectively\n13",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} | [
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] |
2311.04254 | 52 | 13
predicts these trajectories, indicating its ability to grasp complex thought structures. In the 8-Puzzle example, we observe instances of reflection in the thought structure, with back-and-forth recurrent state transitions. This demonstrates XOTâs capacity for self-reflection, a crucial attribute for LLMs, as discussed in previous work Shinn et al. (2023). In the case of the Pocket Cube, XOT identifies four distinct pathways to reach the goal state, leading to successful problem-solving across multiple solutions.
Overall, these cases highlight how XOT encapsulates the flexibility required in thought generation, fostering diverse and creative thinking for LLMs. This enables them to produce multiple high- quality answers to a single problem effectively.
4.6 EXPERIMENT SUMMARY
In summary, our approach XOT significantly improves the performance of LLMs by introducing a streamlined thought trajectory revision process. This represents a fundamental shift from traditional problem-solving approaches, resulting in substantial performance enhancements across a range of tasks. Notably, XOT excels in solving the Game of 24 and demonstrates its ability to overcome challenges requiring spatial reasoning, such as the 8-Puzzle and Pocket Cube, which were previously challenging for LLMs. The remarkable synergy of improved performance, efficiency, and flexibility exhibited by XOT positions it as an exemplary and superior method for eliciting optimal responses from LLMs.
5 RELATED WORK | 2311.04254#52 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 52,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "13\npredicts these trajectories, indicating its ability to grasp complex thought structures. In the 8-Puzzle example, we observe instances of reflection in the thought structure, with back-and-forth recurrent state transitions. This demonstrates XOTâs capacity for self-reflection, a crucial attribute for LLMs, as discussed in previous work Shinn et al. (2023). In the case of the Pocket Cube, XOT identifies four distinct pathways to reach the goal state, leading to successful problem-solving across multiple solutions.\nOverall, these cases highlight how XOT encapsulates the flexibility required in thought generation, fostering diverse and creative thinking for LLMs. This enables them to produce multiple high- quality answers to a single problem effectively.\n4.6 EXPERIMENT SUMMARY\nIn summary, our approach XOT significantly improves the performance of LLMs by introducing a streamlined thought trajectory revision process. This represents a fundamental shift from traditional problem-solving approaches, resulting in substantial performance enhancements across a range of tasks. Notably, XOT excels in solving the Game of 24 and demonstrates its ability to overcome challenges requiring spatial reasoning, such as the 8-Puzzle and Pocket Cube, which were previously challenging for LLMs. The remarkable synergy of improved performance, efficiency, and flexibility exhibited by XOT positions it as an exemplary and superior method for eliciting optimal responses from LLMs.\n5 RELATED WORK",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} | [
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] |
2311.04254 | 53 | 5 RELATED WORK
Decision Making & Planning with LLMs. The utilization of LLMs for decision-making and plan- ning has become a prominent area of research. Similar to human problem-solving, the process in- volves breaking down complex problems into sub-tasks. Various frameworks, such as CoT Wei et al. (2022), ToT Yao et al. (2023), and GoT Besta et al. (2023), have been designed to facilitate prob- lem decomposition in different structural forms, leading to enhanced solutions derived from LLMs. Extensions of these frameworks have also been explored across different domains and modalities Zhang et al. (2022; 2023); Ning et al. (2023); Turpin et al. (2023); Long (2023). Our approach XOT distinguishes itself from the aforementioned work by concurrently achieving superior performance, efficiency, and flexibility, embodying the concept of comprehensive thought generation. | 2311.04254#53 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 53,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "5 RELATED WORK\nDecision Making & Planning with LLMs. The utilization of LLMs for decision-making and plan- ning has become a prominent area of research. Similar to human problem-solving, the process in- volves breaking down complex problems into sub-tasks. Various frameworks, such as CoT Wei et al. (2022), ToT Yao et al. (2023), and GoT Besta et al. (2023), have been designed to facilitate prob- lem decomposition in different structural forms, leading to enhanced solutions derived from LLMs. Extensions of these frameworks have also been explored across different domains and modalities Zhang et al. (2022; 2023); Ning et al. (2023); Turpin et al. (2023); Long (2023). Our approach XOT distinguishes itself from the aforementioned work by concurrently achieving superior performance, efficiency, and flexibility, embodying the concept of comprehensive thought generation.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} | [
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2311.04254 | 54 | Furthermore, the âDescribe, Explain, Plan, and Selectâ framework introduced in Wang et al. (2023b) presents an interactive planning approach for LLMs, significantly enhancing planning performance for multi-task agents. Research conducted in Singh et al. (2023) leverages LLMs to suggest next actions or sequences during task planning for robotics, leading to improved task performance across various metrics. Additionally, work presented in Xie et al. (2023) employs LLMs to translate natural language into planning goals, demonstrating their capacity to harness commonsense knowledge and reasoning to provide missing details for under-specified goals. These studies underscore the growing potential of LLMs in the field of planning, with research efforts expanding rapidly. | 2311.04254#54 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 54,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "Furthermore, the âDescribe, Explain, Plan, and Selectâ framework introduced in Wang et al. (2023b) presents an interactive planning approach for LLMs, significantly enhancing planning performance for multi-task agents. Research conducted in Singh et al. (2023) leverages LLMs to suggest next actions or sequences during task planning for robotics, leading to improved task performance across various metrics. Additionally, work presented in Xie et al. (2023) employs LLMs to translate natural language into planning goals, demonstrating their capacity to harness commonsense knowledge and reasoning to provide missing details for under-specified goals. These studies underscore the growing potential of LLMs in the field of planning, with research efforts expanding rapidly.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} | [
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] |
2311.04254 | 55 | Augmenting LLMs with RL. Enhancing the capabilities of LLMs through the incorporation of ex- ternal models constitutes an effective strategy for improving their overall quality. The foundational work of ChatGPT Ouyang et al. (2022) leverages RL from human feedback to enable LLMs to ad- here to human guidance, resulting in a substantial enhancement of their truthfulness and a reduction in toxic output. Similarly, GLAM Carta et al. (2023) employs online RL to establish alignment between LLMsâ knowledge and the broader environment, thus enhancing their ability to generalize to new objects or tasks and ultimately improving their performance. Additionally, an interesting study in Yuan et al. (2023) utilizes RL to acquire basic skills in the context of Minecraft Cipollone et al. (2014), with subsequent high-level planning carried out by LLMs. This approach demon- strates promising performance across various Minecraft tasks. Furthermore, the ESPER framework Yu et al. (2023) harnesses RL to achieve alignment between multimodal inputs and language model generations, all without the need for direct supervision. This empowers LLMs to effectively tackle multimodal tasks and provides robust visual alignment and rapid inference speeds while preserving the textual domain. Collectively, these research endeavors underscore the considerable potential in augmenting LLMs with reinforcement learning techniques.
14
# 6 DISCUSSION | 2311.04254#55 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 55,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "Augmenting LLMs with RL. Enhancing the capabilities of LLMs through the incorporation of ex- ternal models constitutes an effective strategy for improving their overall quality. The foundational work of ChatGPT Ouyang et al. (2022) leverages RL from human feedback to enable LLMs to ad- here to human guidance, resulting in a substantial enhancement of their truthfulness and a reduction in toxic output. Similarly, GLAM Carta et al. (2023) employs online RL to establish alignment between LLMsâ knowledge and the broader environment, thus enhancing their ability to generalize to new objects or tasks and ultimately improving their performance. Additionally, an interesting study in Yuan et al. (2023) utilizes RL to acquire basic skills in the context of Minecraft Cipollone et al. (2014), with subsequent high-level planning carried out by LLMs. This approach demon- strates promising performance across various Minecraft tasks. Furthermore, the ESPER framework Yu et al. (2023) harnesses RL to achieve alignment between multimodal inputs and language model generations, all without the need for direct supervision. This empowers LLMs to effectively tackle multimodal tasks and provides robust visual alignment and rapid inference speeds while preserving the textual domain. Collectively, these research endeavors underscore the considerable potential in augmenting LLMs with reinforcement learning techniques.\n14\n# 6 DISCUSSION",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} | [
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] |
2311.04254 | 56 | 14
# 6 DISCUSSION
Generalization While XOT is presently utilized for reasoning and search problems, its applicability can be extended to a broader spectrum of problem domains characterized by decomposable tasks with well-defined objectives. The MCTS utilized in XOT is particularly suitable for such tasks and can therefore generalize to more complex problems. We also note that MCTS is functioning in a supportive role and can be substituted with alternative supervised or RL models for thought exploration and generation, which can serve as a copilot to inject domain knowledge of the real- world model to LLMs. This opens up a promising avenue for future research, enabling LLMs to engage in more effective planning and problem solving processes. | 2311.04254#56 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 56,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "14\n# 6 DISCUSSION\nGeneralization While XOT is presently utilized for reasoning and search problems, its applicability can be extended to a broader spectrum of problem domains characterized by decomposable tasks with well-defined objectives. The MCTS utilized in XOT is particularly suitable for such tasks and can therefore generalize to more complex problems. We also note that MCTS is functioning in a supportive role and can be substituted with alternative supervised or RL models for thought exploration and generation, which can serve as a copilot to inject domain knowledge of the real- world model to LLMs. This opens up a promising avenue for future research, enabling LLMs to engage in more effective planning and problem solving processes.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} | [
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2311.04254 | 57 | Limitation We also note that the implementation of XOT necessitates the training of additional pol- icy and value models to expedite the inference process. This training process requires the acquisition of datasets from real-world environments, introducing supplementary costs and efforts. However, note that these policy and value models are considerably smaller and more computationally efficient than the underlying LLMs. Consequently, the incurred costs are deemed low, particularly in the con- text of tasks featured in this study, where the thought steps and objectives are well-defined. In future research endeavors, we intend to explore methods to enhance the efficiency of the training process for XOT in scenarios where the objectives are less straightforward, such as multi-agent planning and code generation tasks Talebirad & Nadiri (2023); Vaithilingam et al. (2022). This endeavor will expand the applicability of the proposed XOT framework to a broader range of applications. | 2311.04254#57 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 57,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "Limitation We also note that the implementation of XOT necessitates the training of additional pol- icy and value models to expedite the inference process. This training process requires the acquisition of datasets from real-world environments, introducing supplementary costs and efforts. However, note that these policy and value models are considerably smaller and more computationally efficient than the underlying LLMs. Consequently, the incurred costs are deemed low, particularly in the con- text of tasks featured in this study, where the thought steps and objectives are well-defined. In future research endeavors, we intend to explore methods to enhance the efficiency of the training process for XOT in scenarios where the objectives are less straightforward, such as multi-agent planning and code generation tasks Talebirad & Nadiri (2023); Vaithilingam et al. (2022). This endeavor will expand the applicability of the proposed XOT framework to a broader range of applications.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} | [
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] |
2311.04254 | 58 | Conclusion The XOT framework presented in this paper signifies a significant progression in It challenges the constraints of thought generation for LLMs aimed at solving complex tasks. the âPenrose Triangle â by concurrently achieving performance, efficiency, and flexibility, a feat unattainable by existing prompting paradigms. This accomplishment is achieved through the inte- gration of MCTS with pretrained low-cost policy and value networks, by injecting domain knowl- edge into LLMs, offloading thought searching, and facilitating unconstrained free-style thought ex- ploration. The collaborative thought revision framework involving MCTS and LLM further en- hances the quality of thought generation. Experimental evaluations conducted across three intricate real-world problems, namely the Game of 24, 8-Puzzle, and Pocket Cube, provide empirical evi- dence that our XOT framework significantly outperforms existing prompting paradigms, particularly in scenarios involving multi-solution problems. | 2311.04254#58 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 58,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "Conclusion The XOT framework presented in this paper signifies a significant progression in It challenges the constraints of thought generation for LLMs aimed at solving complex tasks. the âPenrose Triangle â by concurrently achieving performance, efficiency, and flexibility, a feat unattainable by existing prompting paradigms. This accomplishment is achieved through the inte- gration of MCTS with pretrained low-cost policy and value networks, by injecting domain knowl- edge into LLMs, offloading thought searching, and facilitating unconstrained free-style thought ex- ploration. The collaborative thought revision framework involving MCTS and LLM further en- hances the quality of thought generation. Experimental evaluations conducted across three intricate real-world problems, namely the Game of 24, 8-Puzzle, and Pocket Cube, provide empirical evi- dence that our XOT framework significantly outperforms existing prompting paradigms, particularly in scenarios involving multi-solution problems.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} | [
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] |
2311.04254 | 59 | # REFERENCES
4 Numbers. https://www.4nums.com/game/difficulties/. [Online; accessed 21-Sep- 2023].
I Calculated ChatGPTâs IQ. https://www.youtube.com/watch?v=HXb9Azzhr1k. Ac- cessed: 2023-10-30.
Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Lukas Gianinazzi, Joanna Gajda, Tomasz Lehmann, Michal Podstawski, Hubert Niewiadomski, Piotr Nyczyk, et al. Graph of thoughts: Solving elaborate problems with large language models. arXiv preprint arXiv:2308.09687, 2023.
Thomas Carta, Cl´ement Romac, Thomas Wolf, Sylvain Lamprier, Olivier Sigaud, and Pierre-Yves Oudeyer. Grounding large language models in interactive environments with online reinforcement learning. arXiv preprint arXiv:2302.02662, 2023. | 2311.04254#59 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 59,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "# REFERENCES\n4 Numbers. https://www.4nums.com/game/difficulties/. [Online; accessed 21-Sep- 2023].\nI Calculated ChatGPTâs IQ. https://www.youtube.com/watch?v=HXb9Azzhr1k. Ac- cessed: 2023-10-30.\nMaciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Lukas Gianinazzi, Joanna Gajda, Tomasz Lehmann, Michal Podstawski, Hubert Niewiadomski, Piotr Nyczyk, et al. Graph of thoughts: Solving elaborate problems with large language models. arXiv preprint arXiv:2308.09687, 2023.\nThomas Carta, Cl´ement Romac, Thomas Wolf, Sylvain Lamprier, Olivier Sigaud, and Pierre-Yves Oudeyer. Grounding large language models in interactive environments with online reinforcement learning. arXiv preprint arXiv:2302.02662, 2023.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} | [
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] |
2311.04254 | 60 | Yinfang Chen, Huaibing Xie, Minghua Ma, Yu Kang, Xin Gao, Liu Shi, Yunjie Cao, Xuedong Gao, Hao Fan, Ming Wen, et al. Empowering practical root cause analysis by large language models for cloud incidents. arXiv preprint arXiv:2305.15778, 2023.
Maria Cipollone, Catherine C Schifter, and Rick A Moffat. Minecraft as a creative tool: A case study. International Journal of Game-Based Learning (IJGBL), 4(2):1â14, 2014.
Erik D Demaine, Sarah Eisenstat, and Mikhail Rudoy. Solving the rubikâs cube optimally is np- complete. arXiv preprint arXiv:1706.06708, 2017.
15
Haakon Faste and Honray Lin. The untapped promise of digital mind maps. In Proceedings of the SIGCHI conference on human factors in computing systems, pp. 1017â1026, 2012.
Simon Frieder, Luca Pinchetti, Ryan-Rhys Griffiths, Tommaso Salvatori, Thomas Lukasiewicz, Philipp Christian Petersen, Alexis Chevalier, and Julius Berner. Mathematical capabilities of chatgpt. arXiv preprint arXiv:2301.13867, 2023. | 2311.04254#60 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
{
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 60,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "Yinfang Chen, Huaibing Xie, Minghua Ma, Yu Kang, Xin Gao, Liu Shi, Yunjie Cao, Xuedong Gao, Hao Fan, Ming Wen, et al. Empowering practical root cause analysis by large language models for cloud incidents. arXiv preprint arXiv:2305.15778, 2023.\nMaria Cipollone, Catherine C Schifter, and Rick A Moffat. Minecraft as a creative tool: A case study. International Journal of Game-Based Learning (IJGBL), 4(2):1â14, 2014.\nErik D Demaine, Sarah Eisenstat, and Mikhail Rudoy. Solving the rubikâs cube optimally is np- complete. arXiv preprint arXiv:1706.06708, 2017.\n15\nHaakon Faste and Honray Lin. The untapped promise of digital mind maps. In Proceedings of the SIGCHI conference on human factors in computing systems, pp. 1017â1026, 2012.\nSimon Frieder, Luca Pinchetti, Ryan-Rhys Griffiths, Tommaso Salvatori, Thomas Lukasiewicz, Philipp Christian Petersen, Alexis Chevalier, and Julius Berner. Mathematical capabilities of chatgpt. arXiv preprint arXiv:2301.13867, 2023.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} | [
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] |
2311.04254 | 61 | Aur´elien Garivier and Eric Moulines. On upper-confidence bound policies for switching bandit problems. In International Conference on Algorithmic Learning Theory, pp. 174â188. Springer, 2011.
Peter Jamieson. Using modern graph analysis techniques on mind maps to help quantify learning. In 2012 Frontiers in Education Conference Proceedings, pp. 1â6. IEEE, 2012.
Emre Kıcıman, Robert Ness, Amit Sharma, and Chenhao Tan. Causal reasoning and large language models: Opening a new frontier for causality. arXiv preprint arXiv:2305.00050, 2023.
Yuxi Li. Deep reinforcement learning: An overview. arXiv preprint arXiv:1701.07274, 2017.
Jieyi Long. Large language model guided tree-of-thought. arXiv preprint arXiv:2305.08291, 2023.
Xuefei Ning, Zinan Lin, Zixuan Zhou, Huazhong Yang, and Yu Wang. Skeleton-of-thought: Large language models can do parallel decoding. arXiv preprint arXiv:2307.15337, 2023. | 2311.04254#61 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 61,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "Aur´elien Garivier and Eric Moulines. On upper-confidence bound policies for switching bandit problems. In International Conference on Algorithmic Learning Theory, pp. 174â188. Springer, 2011.\nPeter Jamieson. Using modern graph analysis techniques on mind maps to help quantify learning. In 2012 Frontiers in Education Conference Proceedings, pp. 1â6. IEEE, 2012.\nEmre Kıcıman, Robert Ness, Amit Sharma, and Chenhao Tan. Causal reasoning and large language models: Opening a new frontier for causality. arXiv preprint arXiv:2305.00050, 2023.\nYuxi Li. Deep reinforcement learning: An overview. arXiv preprint arXiv:1701.07274, 2017.\nJieyi Long. Large language model guided tree-of-thought. arXiv preprint arXiv:2305.08291, 2023.\nXuefei Ning, Zinan Lin, Zixuan Zhou, Huazhong Yang, and Yu Wang. Skeleton-of-thought: Large language models can do parallel decoding. arXiv preprint arXiv:2307.15337, 2023.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} | [
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2311.04254 | 62 | Reham Omar, Omij Mangukiya, Panos Kalnis, and Essam Mansour. Chatgpt versus traditional question answering for knowledge graphs: Current status and future directions towards knowl- edge graph chatbots. arXiv preprint arXiv:2302.06466, 2023.
OpenAI. Gpt-4 technical report, 2023.
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35: 27730â27744, 2022.
Martin L Puterman. Markov decision processes. Handbooks in operations research and management science, 2:331â434, 1990.
Daniel Ratner and Manfred Warmuth. Finding a shortest solution for the n x n extension of the In Proceedings of the Fifth AAAI National Conference on Artificial 15-puzzle is intractable. Intelligence, pp. 168â172, 1986.
Christopher D Rosin. Multi-armed bandits with episode context. Annals of Mathematics and Artifi- cial Intelligence, 61(3):203â230, 2011. | 2311.04254#62 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 62,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "Reham Omar, Omij Mangukiya, Panos Kalnis, and Essam Mansour. Chatgpt versus traditional question answering for knowledge graphs: Current status and future directions towards knowl- edge graph chatbots. arXiv preprint arXiv:2302.06466, 2023.\nOpenAI. Gpt-4 technical report, 2023.\nLong Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35: 27730â27744, 2022.\nMartin L Puterman. Markov decision processes. Handbooks in operations research and management science, 2:331â434, 1990.\nDaniel Ratner and Manfred Warmuth. Finding a shortest solution for the n x n extension of the In Proceedings of the Fifth AAAI National Conference on Artificial 15-puzzle is intractable. Intelligence, pp. 168â172, 1986.\nChristopher D Rosin. Multi-armed bandits with episode context. Annals of Mathematics and Artifi- cial Intelligence, 61(3):203â230, 2011.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} | [
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2311.04254 | 63 | Christopher D Rosin. Multi-armed bandits with episode context. Annals of Mathematics and Artifi- cial Intelligence, 61(3):203â230, 2011.
Noah Shinn, Beck Labash, and Ashwin Gopinath. Reflexion: an autonomous agent with dynamic memory and self-reflection. arXiv preprint arXiv:2303.11366, 2023.
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al. Mastering the game of go without human knowledge. nature, 550(7676):354â359, 2017.
Ishika Singh, Valts Blukis, Arsalan Mousavian, Ankit Goyal, Danfei Xu, Jonathan Tremblay, Dieter Fox, Jesse Thomason, and Animesh Garg. Progprompt: Generating situated robot task plans using In 2023 IEEE International Conference on Robotics and Automation large language models. (ICRA), pp. 11523â11530. IEEE, 2023. | 2311.04254#63 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 63,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "Christopher D Rosin. Multi-armed bandits with episode context. Annals of Mathematics and Artifi- cial Intelligence, 61(3):203â230, 2011.\nNoah Shinn, Beck Labash, and Ashwin Gopinath. Reflexion: an autonomous agent with dynamic memory and self-reflection. arXiv preprint arXiv:2303.11366, 2023.\nDavid Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al. Mastering the game of go without human knowledge. nature, 550(7676):354â359, 2017.\nIshika Singh, Valts Blukis, Arsalan Mousavian, Ankit Goyal, Danfei Xu, Jonathan Tremblay, Dieter Fox, Jesse Thomason, and Animesh Garg. Progprompt: Generating situated robot task plans using In 2023 IEEE International Conference on Robotics and Automation large language models. (ICRA), pp. 11523â11530. IEEE, 2023.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} | [
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2311.04254 | 64 | Kaya Stechly, Matthew Marquez, and Subbarao Kambhampati. Gpt-4 doesnât know itâs wrong: An analysis of iterative prompting for reasoning problems. arXiv preprint arXiv:2310.12397, 2023.
Yashar Talebirad and Amirhossein Nadiri. Multi-agent collaboration: Harnessing the power of intelligent llm agents. arXiv preprint arXiv:2306.03314, 2023.
16
Miles Turpin, Julian Michael, Ethan Perez, and Samuel R Bowman. Language models donât al- ways say what they think: Unfaithful explanations in chain-of-thought prompting. arXiv preprint arXiv:2305.04388, 2023.
Priyan Vaithilingam, Tianyi Zhang, and Elena L Glassman. Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In Chi conference on human factors in computing systems extended abstracts, pp. 1â7, 2022.
Karthik Valmeekam, Matthew Marquez, and Subbarao Kambhampati. Can large language models really improve by self-critiquing their own plans? arXiv preprint arXiv:2310.08118, 2023. | 2311.04254#64 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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] | {
"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 64,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "Kaya Stechly, Matthew Marquez, and Subbarao Kambhampati. Gpt-4 doesnât know itâs wrong: An analysis of iterative prompting for reasoning problems. arXiv preprint arXiv:2310.12397, 2023.\nYashar Talebirad and Amirhossein Nadiri. Multi-agent collaboration: Harnessing the power of intelligent llm agents. arXiv preprint arXiv:2306.03314, 2023.\n16\nMiles Turpin, Julian Michael, Ethan Perez, and Samuel R Bowman. Language models donât al- ways say what they think: Unfaithful explanations in chain-of-thought prompting. arXiv preprint arXiv:2305.04388, 2023.\nPriyan Vaithilingam, Tianyi Zhang, and Elena L Glassman. Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In Chi conference on human factors in computing systems extended abstracts, pp. 1â7, 2022.\nKarthik Valmeekam, Matthew Marquez, and Subbarao Kambhampati. Can large language models really improve by self-critiquing their own plans? arXiv preprint arXiv:2310.08118, 2023.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} | [
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2311.04254 | 65 | Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V Le, Ed H. Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. Self-consistency improves chain of thought reasoning in language models. In The Eleventh International Conference on Learning Representations, 2023a.
Zihao Wang, Shaofei Cai, Anji Liu, Xiaojian Ma, and Yitao Liang. Describe, explain, plan and select: Interactive planning with large language models enables open-world multi-task agents. arXiv preprint arXiv:2302.01560, 2023b.
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 35:24824â24837, 2022.
Yaqi Xie, Chen Yu, Tongyao Zhu, Jinbin Bai, Ze Gong, and Harold Soh. Translating natural lan- guage to planning goals with large-language models. arXiv preprint arXiv:2302.05128, 2023. | 2311.04254#65 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 65,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V Le, Ed H. Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. Self-consistency improves chain of thought reasoning in language models. In The Eleventh International Conference on Learning Representations, 2023a.\nZihao Wang, Shaofei Cai, Anji Liu, Xiaojian Ma, and Yitao Liang. Describe, explain, plan and select: Interactive planning with large language models enables open-world multi-task agents. arXiv preprint arXiv:2302.01560, 2023b.\nJason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 35:24824â24837, 2022.\nYaqi Xie, Chen Yu, Tongyao Zhu, Jinbin Bai, Ze Gong, and Harold Soh. Translating natural lan- guage to planning goals with large-language models. arXiv preprint arXiv:2302.05128, 2023.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} | [
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] |
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Zhuosheng Zhang, Aston Zhang, Mu Li, and Alex Smola. Automatic chain of thought prompting in large language models. arXiv preprint arXiv:2210.03493, 2022. | 2311.04254#66 | Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation | Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains. | http://arxiv.org/pdf/2311.04254 | Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang | cs.AI, cs.LG | 17 pages, 5 figures | null | cs.AI | 20231107 | 20231112 | [
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"authors": "Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang",
"chunk_id": 66,
"doc_id": "2311.04254",
"primary_category": "cs.AI",
"published": 20231107,
"source": "http://arxiv.org/pdf/2311.04254",
"summary": "Recent advancements in Large Language Models (LLMs) have revolutionized\ndecision-making by breaking down complex problems into more manageable language\nsequences referred to as ``thoughts''. An effective thought design should\nconsider three key perspectives: performance, efficiency, and flexibility.\nHowever, existing thought can at most exhibit two of these attributes. To\naddress these limitations, we introduce a novel thought prompting approach\ncalled ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle\nof existing thought paradigms. XoT leverages pretrained reinforcement learning\nand Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge\ninto thoughts, thereby enhancing LLMs' capabilities and enabling them to\ngeneralize to unseen problems efficiently. Through the utilization of the\nMCTS-LLM collaborative thought revision framework, this approach autonomously\nproduces high-quality comprehensive cognitive mappings with minimal LLM\ninteractions. Additionally, XoT empowers LLMs to engage in unconstrained\nthinking, allowing for flexible cognitive mappings for problems with multiple\nsolutions. We evaluate XoT on several challenging multi-solution\nproblem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our\nresults demonstrate that XoT significantly outperforms existing approaches.\nNotably, XoT can yield multiple solutions with just one LLM call, showcasing\nits remarkable proficiency in addressing complex problems across diverse\ndomains.",
"text": "Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L Griffiths, Yuan Cao, and Karthik Narasimhan. Tree of thoughts: Deliberate problem solving with large language models. arXiv preprint arXiv:2305.10601, 2023.\nYoungjae Yu, Jiwan Chung, Heeseung Yun, Jack Hessel, Jae Sung Park, Ximing Lu, Rowan Zellers, Prithviraj Ammanabrolu, Ronan Le Bras, Gunhee Kim, et al. Fusing pre-trained language mod- els with multimodal prompts through reinforcement learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10845â10856, 2023.\nHaoqi Yuan, Chi Zhang, Hongcheng Wang, Feiyang Xie, Penglin Cai, Hao Dong, and Zongqing Lu. Plan4mc: Skill reinforcement learning and planning for open-world minecraft tasks. arXiv preprint arXiv:2303.16563, 2023.\nZhuosheng Zhang, Aston Zhang, Mu Li, and Alex Smola. Automatic chain of thought prompting in large language models. arXiv preprint arXiv:2210.03493, 2022.",
"title": "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation",
"year": 2023
} | [
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] |
2311.01343 | 0 | 3 2 0 2 v o N 8 ] R I . s c [
3 v 3 4 3 1 0 . 1 1 3 2 : v i X r a
Collaborative Large Language Model for Recommender Systems Yaochen Zhuâ,1, Liang Wu2, Qi Guo2, Liangjie Hong2, Jundong Li1 1University of Virginia, 2LinkedIn Inc. 1{uqp4qh, jundong}@virginia.edu, 2{liawu, qguo, liahong}@linkedin.com
Liangjie Hongâ, Jundong Li! âLinkedIn Inc. qguo, liahong}@linkedin.com Recommendations QO Yes! tT retrieve few tural Language e.g., user t transform user interactions and features item 2 is a computer. (continuous or categorical) will user_1 buy a mouse? Thouse is a component of PC maybe she needs a mouse encoded knowledge reasoning ability has bought item 2. [e) oho is a CS student.
# ABSTRACT | 2311.01343#0 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
{
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"id": "2308.10053"
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{
"id": "2205.08084"
},
{
"id": "2303.14524"
},
{
"id": "2305.07001"
},
{
"id": "2303.13835"
},
{
"id": "2303.18223"
},
{
"id": "2302.03735"
},
{
"id": "2305.00447"
},
{
"id": "2305.08845"
},
{
"id": "2104.08691"
},
{
"id": "2308.10837"
},
{
"id": "2305.06569"
}
] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 0,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "3 2 0 2 v o N 8 ] R I . s c [\n3 v 3 4 3 1 0 . 1 1 3 2 : v i X r a\nCollaborative Large Language Model for Recommender Systems Yaochen Zhuâ,1, Liang Wu2, Qi Guo2, Liangjie Hong2, Jundong Li1 1University of Virginia, 2LinkedIn Inc. 1{uqp4qh, jundong}@virginia.edu, 2{liawu, qguo, liahong}@linkedin.com\nLiangjie Hongâ, Jundong Li! âLinkedIn Inc. qguo, liahong}@linkedin.com Recommendations QO Yes! tT retrieve few tural Language e.g., user t transform user interactions and features item 2 is a computer. (continuous or categorical) will user_1 buy a mouse? Thouse is a component of PC maybe she needs a mouse encoded knowledge reasoning ability has bought item 2. [e) oho is a CS student.\n# ABSTRACT",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} | [
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2311.04915 | 0 | # Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models
Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, & Sowon Hahn Human Factors Psychology Lab Seoul National University [email protected], [email protected]
Standard Prompting Input: | just broke up. My life is over. Output: | am sorry to hear that you are sad Chain-of-Empathy Prompting (CoE) Input: | just broke up. My life is over, 1st Step : The client is feeling sad. Reasoning : (Emotion) (CoE) 2nd Step : The client shows (Cognitive catastrophic cognitive error. Error) Output :'m sorry to hear that. | understand you're feeling extremely upset due to your breakup. It's natural to) feel this way. Breakups are hard but they don't define) your whole existence or future. Let's work through! these emotions and explore ways to cope, Psychotherapy-based CoEs Focus "Cognitive Error" "Emotion Control" }-â2"Self-Understanding" }-â2"Decision Making"
Figure 1: Chain-of-Empathy (CoE) prompting with cognitive reasoning of humanâs emotion based on psychotherapy models.
# Abstract | 2311.04915#0 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI. | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | [
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"authors": "Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn",
"chunk_id": 0,
"doc_id": "2311.04915",
"primary_category": "cs.CL",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.04915",
"summary": "We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psychotherapy to induce Large Language Models (LLMs) to\nreason about human emotional states. This method is inspired by various\npsychotherapy approaches including Cognitive Behavioral Therapy (CBT),\nDialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality\nTherapy (RT), each leading to different patterns of interpreting clients'\nmental states. LLMs without reasoning generated predominantly exploratory\nresponses. However, when LLMs used CoE reasoning, we found a more comprehensive\nrange of empathetic responses aligned with the different reasoning patterns of\neach psychotherapy model. The CBT based CoE resulted in the most balanced\ngeneration of empathetic responses. The findings underscore the importance of\nunderstanding the emotional context and how it affects human and AI\ncommunication. Our research contributes to understanding how psychotherapeutic\nmodels can be incorporated into LLMs, facilitating the development of\ncontext-specific, safer, and empathetic AI.",
"text": "# Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models\nYoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, & Sowon Hahn Human Factors Psychology Lab Seoul National University [email protected], [email protected]\nStandard Prompting Input: | just broke up. My life is over. Output: | am sorry to hear that you are sad Chain-of-Empathy Prompting (CoE) Input: | just broke up. My life is over, 1st Step : The client is feeling sad. Reasoning : (Emotion) (CoE) 2nd Step : The client shows (Cognitive catastrophic cognitive error. Error) Output :'m sorry to hear that. | understand you're feeling extremely upset due to your breakup. It's natural to) feel this way. Breakups are hard but they don't define) your whole existence or future. Let's work through! these emotions and explore ways to cope, Psychotherapy-based CoEs Focus \"Cognitive Error\" \"Emotion Control\" }-â2\"Self-Understanding\" }-â2\"Decision Making\"\n Figure 1: Chain-of-Empathy (CoE) prompting with cognitive reasoning of humanâs emotion based on psychotherapy models. \n# Abstract",
"title": "Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models",
"year": 2023
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2311.01343 | 1 | Recently, there is a growing interest in developing next-generation recommender systems (RSs) based on pretrained large language models (LLMs), fully utilizing their encoded knowledge and reason- ing ability. However, the semantic gap between natural language and recommendation tasks is still not well addressed, leading to multiple issues such as spuriously-correlated user/item descriptors, ineffective language modeling on user/item contents, and ineffi- cient recommendations via auto-regression, etc. In this paper, we propose CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and ID paradigm of RS, aiming to address the above challenges simultaneously. We first extend the vocabulary of pretrained LLMs with user/item ID tokens to faithfully model the user/item collaborative and content semantics. Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is proposed to effectively learn user/item collaborative/content token embeddings via language modeling on RS-specific corpora estab- lished from user-item interactions and user/item features, where each document is split into a prompt consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and | 2311.01343#1 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 1,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "Recently, there is a growing interest in developing next-generation recommender systems (RSs) based on pretrained large language models (LLMs), fully utilizing their encoded knowledge and reason- ing ability. However, the semantic gap between natural language and recommendation tasks is still not well addressed, leading to multiple issues such as spuriously-correlated user/item descriptors, ineffective language modeling on user/item contents, and ineffi- cient recommendations via auto-regression, etc. In this paper, we propose CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and ID paradigm of RS, aiming to address the above challenges simultaneously. We first extend the vocabulary of pretrained LLMs with user/item ID tokens to faithfully model the user/item collaborative and content semantics. Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is proposed to effectively learn user/item collaborative/content token embeddings via language modeling on RS-specific corpora estab- lished from user-item interactions and user/item features, where each document is split into a prompt consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
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2311.04915 | 1 | Figure 1: Chain-of-Empathy (CoE) prompting with cognitive reasoning of humanâs emotion based on psychotherapy models.
# Abstract
We present a novel method, the Chain of Empathy that utilizes insights from psychotherapy to induce Large Language Models (LLMs) to reason about human emotional states. This method is inspired psychotherapy various approachesâCognitive-Behavioral Therapy (CBT), Dialectical Behavior Therapy (DBT), Person-Centered Therapy (PCT), and Reality Therapy to different patterns of interpreting clientsâ mental states. generated LLMs predominantly exploratory responses. However, when LLMs used CoE reasoning, we found a more comprehensive range of empathetic responses aligned with each psychotherapy modelâs different reasoning patterns. The CBT- based CoE resulted in the most balanced responses. The generation of empathetic importance of the findings underscore understanding the emotional context and how it affects human-AI communication. Our research
contributes how psychotherapeutic models can be incorporated into LLMs, facilitating the development of context-specific, safer, and empathetic AI.
# 1. Introduction | 2311.04915#1 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI. | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | [
{
"id": "2302.13971"
},
{
"id": "2305.10601"
},
{
"id": "2212.14402"
},
{
"id": "2205.11916"
},
{
"id": "2108.07258"
},
{
"id": "2209.08141"
},
{
"id": "2201.11903"
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{
"id": "2306.08997"
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] | {
"authors": "Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn",
"chunk_id": 1,
"doc_id": "2311.04915",
"primary_category": "cs.CL",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.04915",
"summary": "We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psychotherapy to induce Large Language Models (LLMs) to\nreason about human emotional states. This method is inspired by various\npsychotherapy approaches including Cognitive Behavioral Therapy (CBT),\nDialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality\nTherapy (RT), each leading to different patterns of interpreting clients'\nmental states. LLMs without reasoning generated predominantly exploratory\nresponses. However, when LLMs used CoE reasoning, we found a more comprehensive\nrange of empathetic responses aligned with the different reasoning patterns of\neach psychotherapy model. The CBT based CoE resulted in the most balanced\ngeneration of empathetic responses. The findings underscore the importance of\nunderstanding the emotional context and how it affects human and AI\ncommunication. Our research contributes to understanding how psychotherapeutic\nmodels can be incorporated into LLMs, facilitating the development of\ncontext-specific, safer, and empathetic AI.",
"text": "Figure 1: Chain-of-Empathy (CoE) prompting with cognitive reasoning of humanâs emotion based on psychotherapy models. \n# Abstract\nWe present a novel method, the Chain of Empathy that utilizes insights from psychotherapy to induce Large Language Models (LLMs) to reason about human emotional states. This method is inspired psychotherapy various approachesâCognitive-Behavioral Therapy (CBT), Dialectical Behavior Therapy (DBT), Person-Centered Therapy (PCT), and Reality Therapy to different patterns of interpreting clientsâ mental states. generated LLMs predominantly exploratory responses. However, when LLMs used CoE reasoning, we found a more comprehensive range of empathetic responses aligned with each psychotherapy modelâs different reasoning patterns. The CBT- based CoE resulted in the most balanced responses. The generation of empathetic importance of the findings underscore understanding the emotional context and how it affects human-AI communication. Our research\ncontributes how psychotherapeutic models can be incorporated into LLMs, facilitating the development of context-specific, safer, and empathetic AI.\n# 1. Introduction",
"title": "Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models",
"year": 2023
} | [
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2311.01343 | 2 | and user/item features, where each document is split into a prompt consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and a main text con- sisting of homogeneous item tokens or vocab tokens that facilitates stable and effective language modeling. In addition, a novel mutual regularization strategy is introduced to encourage the CLLM4Rec to capture recommendation-oriented information from user/item contents. Finally, we propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where an item prediction head with multinomial likelihood is added to the pretrained CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts established from masked user-item interaction history, where rec- ommendations of multiple items can be generated efficiently1. | 2311.01343#2 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 2,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "and user/item features, where each document is split into a prompt consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and a main text con- sisting of homogeneous item tokens or vocab tokens that facilitates stable and effective language modeling. In addition, a novel mutual regularization strategy is introduced to encourage the CLLM4Rec to capture recommendation-oriented information from user/item contents. Finally, we propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where an item prediction head with multinomial likelihood is added to the pretrained CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts established from masked user-item interaction history, where rec- ommendations of multiple items can be generated efficiently1.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} | [
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2311.04915 | 2 | contributes how psychotherapeutic models can be incorporated into LLMs, facilitating the development of context-specific, safer, and empathetic AI.
# 1. Introduction
(LLMs) have Large Language Models dramatically generation performance that highly resembles human expressions (Brown et al., 2020; Touvron et al., 2023; Taori et al., 2023; Bommasani et al., 2021). These models have been showcasing their reasoning abilities and achieving high performance in various problem-solving tasks, including professional exams such as the bar exam (Bommarito II and Katz, 2022), a math test (Zhang et al., 2023), and medical diagnoses (Nori et al., 2023). Among many recent findings related to LLMs, one interesting point is the introduction of âChain-of-Thought (CoT)â prompting (Wei et al., 2022; Kojima et al., 2022). This method elicits reasoning before generating outputs. Nevertheless, this recent method has primarily experimented with
logical or arithmetic tasks. Whether reasoning about emotional states or underlying causes enhances empathetic responses to user input remains a relatively under-explored area and merits investigation. Empathetic | 2311.04915#2 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI. | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | [
{
"id": "2302.13971"
},
{
"id": "2305.10601"
},
{
"id": "2212.14402"
},
{
"id": "2205.11916"
},
{
"id": "2108.07258"
},
{
"id": "2209.08141"
},
{
"id": "2201.11903"
},
{
"id": "2306.08997"
},
{
"id": "2303.13375"
},
{
"id": "1808.10399"
}
] | {
"authors": "Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn",
"chunk_id": 2,
"doc_id": "2311.04915",
"primary_category": "cs.CL",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.04915",
"summary": "We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psychotherapy to induce Large Language Models (LLMs) to\nreason about human emotional states. This method is inspired by various\npsychotherapy approaches including Cognitive Behavioral Therapy (CBT),\nDialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality\nTherapy (RT), each leading to different patterns of interpreting clients'\nmental states. LLMs without reasoning generated predominantly exploratory\nresponses. However, when LLMs used CoE reasoning, we found a more comprehensive\nrange of empathetic responses aligned with the different reasoning patterns of\neach psychotherapy model. The CBT based CoE resulted in the most balanced\ngeneration of empathetic responses. The findings underscore the importance of\nunderstanding the emotional context and how it affects human and AI\ncommunication. Our research contributes to understanding how psychotherapeutic\nmodels can be incorporated into LLMs, facilitating the development of\ncontext-specific, safer, and empathetic AI.",
"text": "contributes how psychotherapeutic models can be incorporated into LLMs, facilitating the development of context-specific, safer, and empathetic AI.\n# 1. Introduction\n(LLMs) have Large Language Models dramatically generation performance that highly resembles human expressions (Brown et al., 2020; Touvron et al., 2023; Taori et al., 2023; Bommasani et al., 2021). These models have been showcasing their reasoning abilities and achieving high performance in various problem-solving tasks, including professional exams such as the bar exam (Bommarito II and Katz, 2022), a math test (Zhang et al., 2023), and medical diagnoses (Nori et al., 2023). Among many recent findings related to LLMs, one interesting point is the introduction of âChain-of-Thought (CoT)â prompting (Wei et al., 2022; Kojima et al., 2022). This method elicits reasoning before generating outputs. Nevertheless, this recent method has primarily experimented with\nlogical or arithmetic tasks. Whether reasoning about emotional states or underlying causes enhances empathetic responses to user input remains a relatively under-explored area and merits investigation. Empathetic",
"title": "Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models",
"year": 2023
} | [
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2311.01343 | 3 | # Figure 1: Prospective of developing the next generation of recommender systems based on the pretrained LLMs.
[10], such as GPT [11], T5 [12], LlaMA [13], have demonstrated emergent ability when trained on large-scale corpora [14], show- casing an unprecedented understanding of knowledge and patterns contained in natural language [9, 15]. Consequently, it is promising to develop the next generation of RS based on the pretrained LLMs [16], fully utilizing their encoded knowledge, logical reasoning abil- ity, and generative AI power to understand and reason with the user/item semantics and make more accurate recommendations accordingly, especially when users and items are associated with large amounts of textual features, such as biographies, descriptions, content, reviews, and explanations, etc., in modern online platforms [17, 18]. (see Fig. 1 for an intuitive example of an LLM-based RS)
# 1 INTRODUCTION | 2311.01343#3 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
{
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 3,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "# Figure 1: Prospective of developing the next generation of recommender systems based on the pretrained LLMs.\n[10], such as GPT [11], T5 [12], LlaMA [13], have demonstrated emergent ability when trained on large-scale corpora [14], show- casing an unprecedented understanding of knowledge and patterns contained in natural language [9, 15]. Consequently, it is promising to develop the next generation of RS based on the pretrained LLMs [16], fully utilizing their encoded knowledge, logical reasoning abil- ity, and generative AI power to understand and reason with the user/item semantics and make more accurate recommendations accordingly, especially when users and items are associated with large amounts of textual features, such as biographies, descriptions, content, reviews, and explanations, etc., in modern online platforms [17, 18]. (see Fig. 1 for an intuitive example of an LLM-based RS)\n# 1 INTRODUCTION",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} | [
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2311.04915 | 3 | logical or arithmetic tasks. Whether reasoning about emotional states or underlying causes enhances empathetic responses to user input remains a relatively under-explored area and merits investigation. Empathetic
requires cognitive reasoning of othersâ mental states. Different psychotherapeutic approaches offer varied perspectives on empathy (Hofmann et al., 2010; Linehan, 1987; Cooper and McLeod, 2011; Wubbolding et al., 2017). By integrating these approaches into LLMsâ reasoning stage, we can enhance the depth and specificity of their empathetic responses. For this purpose, this study delves into these possibilities and proposes a novel prompting, Chain-of- Empathy prompting (CoE). The CoE prompt integrates a text generation. It focuses on clientsâ emotions and the specific factors leading to those emotions, such as cognitive errors, before generating the output.
# 2. Related Work
# 2.1. Theoretical Backgrounds of Empathy | 2311.04915#3 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI. | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | [
{
"id": "2302.13971"
},
{
"id": "2305.10601"
},
{
"id": "2212.14402"
},
{
"id": "2205.11916"
},
{
"id": "2108.07258"
},
{
"id": "2209.08141"
},
{
"id": "2201.11903"
},
{
"id": "2306.08997"
},
{
"id": "2303.13375"
},
{
"id": "1808.10399"
}
] | {
"authors": "Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn",
"chunk_id": 3,
"doc_id": "2311.04915",
"primary_category": "cs.CL",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.04915",
"summary": "We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psychotherapy to induce Large Language Models (LLMs) to\nreason about human emotional states. This method is inspired by various\npsychotherapy approaches including Cognitive Behavioral Therapy (CBT),\nDialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality\nTherapy (RT), each leading to different patterns of interpreting clients'\nmental states. LLMs without reasoning generated predominantly exploratory\nresponses. However, when LLMs used CoE reasoning, we found a more comprehensive\nrange of empathetic responses aligned with the different reasoning patterns of\neach psychotherapy model. The CBT based CoE resulted in the most balanced\ngeneration of empathetic responses. The findings underscore the importance of\nunderstanding the emotional context and how it affects human and AI\ncommunication. Our research contributes to understanding how psychotherapeutic\nmodels can be incorporated into LLMs, facilitating the development of\ncontext-specific, safer, and empathetic AI.",
"text": "logical or arithmetic tasks. Whether reasoning about emotional states or underlying causes enhances empathetic responses to user input remains a relatively under-explored area and merits investigation. Empathetic\nrequires cognitive reasoning of othersâ mental states. Different psychotherapeutic approaches offer varied perspectives on empathy (Hofmann et al., 2010; Linehan, 1987; Cooper and McLeod, 2011; Wubbolding et al., 2017). By integrating these approaches into LLMsâ reasoning stage, we can enhance the depth and specificity of their empathetic responses. For this purpose, this study delves into these possibilities and proposes a novel prompting, Chain-of- Empathy prompting (CoE). The CoE prompt integrates a text generation. It focuses on clientsâ emotions and the specific factors leading to those emotions, such as cognitive errors, before generating the output.\n# 2. Related Work\n# 2.1. Theoretical Backgrounds of Empathy",
"title": "Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models",
"year": 2023
} | [
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2311.01343 | 4 | # 1 INTRODUCTION
With content growing exponentially on the Web, recommender system (RS) has become an essential component for online service platforms [1]. Nevertheless, since Netflix released its Prize in 2006 [2], RS has long been dominated by the ID-based paradigm, where users and items are represented by unique, continuous ID embed- dings denoting their semantic similarity (e.g., w.r.t. usersâ prefer- ences on items, user/item contents, etc.) [3]. Exemplar ID-based RSs include matrix factorization-based methods such as PMF [4] and the two-tower models [5], where the user/item ID embeddings are either randomly initialized and learned from their historical interactions (i.e., collaborative filtering [6]), or established based on user/item content features (i.e., content-based methods [7, 8]). Recently, large language model (LLM) has become a heated re- search topic that revolutionized both academia and industry [9]. Transformer-based neural networks with billions of parameters | 2311.01343#4 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
{
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 4,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "# 1 INTRODUCTION\nWith content growing exponentially on the Web, recommender system (RS) has become an essential component for online service platforms [1]. Nevertheless, since Netflix released its Prize in 2006 [2], RS has long been dominated by the ID-based paradigm, where users and items are represented by unique, continuous ID embed- dings denoting their semantic similarity (e.g., w.r.t. usersâ prefer- ences on items, user/item contents, etc.) [3]. Exemplar ID-based RSs include matrix factorization-based methods such as PMF [4] and the two-tower models [5], where the user/item ID embeddings are either randomly initialized and learned from their historical interactions (i.e., collaborative filtering [6]), or established based on user/item content features (i.e., content-based methods [7, 8]). Recently, large language model (LLM) has become a heated re- search topic that revolutionized both academia and industry [9]. Transformer-based neural networks with billions of parameters",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} | [
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2311.04915 | 4 | # 2. Related Work
# 2.1. Theoretical Backgrounds of Empathy
Empathy, defined as sharing othersâ emotions and experiences, is a multifaceted concept encompassing cognitive and emotional aspects (Neff, 2003; Anderson and Keltner, 2002; De Vignemont and Singer, 2006; Hall and Schwartz, 2019; Zaki, 2019). Cognitive empathy involves understanding othersâ emotions and perspectives, linked to abilities such as mentalizing and narrative imagination (Eisenberg, 2014). It requires an in-depth cognitive appraisal of the situation, considering factors like pleasantness, control, and certainty of the outcome (Lazarus, 1991; Wondra and (emotional) 2015). Affective Ellsworth, empathy allows to experience individuals othersâ emotions, while motivational empathy, a newer concept, embodies the desire to alleviate othersâ emotional distress (Zaki, 2019).
# 2.2. Empathetic Communication in Text
Natural Language Processing (NLP) has been increasingly developing conversational agents, or chatbots, across various professional domains. These include mental healthcare for victims of crime (Ahn et al., 2020), individuals on the autism spectrum (Diehl et al., 2012), and those suffering from anxiety disorders (Rasouli et al., 2022). | 2311.04915#4 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI. | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | [
{
"id": "2302.13971"
},
{
"id": "2305.10601"
},
{
"id": "2212.14402"
},
{
"id": "2205.11916"
},
{
"id": "2108.07258"
},
{
"id": "2209.08141"
},
{
"id": "2201.11903"
},
{
"id": "2306.08997"
},
{
"id": "2303.13375"
},
{
"id": "1808.10399"
}
] | {
"authors": "Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn",
"chunk_id": 4,
"doc_id": "2311.04915",
"primary_category": "cs.CL",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.04915",
"summary": "We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psychotherapy to induce Large Language Models (LLMs) to\nreason about human emotional states. This method is inspired by various\npsychotherapy approaches including Cognitive Behavioral Therapy (CBT),\nDialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality\nTherapy (RT), each leading to different patterns of interpreting clients'\nmental states. LLMs without reasoning generated predominantly exploratory\nresponses. However, when LLMs used CoE reasoning, we found a more comprehensive\nrange of empathetic responses aligned with the different reasoning patterns of\neach psychotherapy model. The CBT based CoE resulted in the most balanced\ngeneration of empathetic responses. The findings underscore the importance of\nunderstanding the emotional context and how it affects human and AI\ncommunication. Our research contributes to understanding how psychotherapeutic\nmodels can be incorporated into LLMs, facilitating the development of\ncontext-specific, safer, and empathetic AI.",
"text": "# 2. Related Work\n# 2.1. Theoretical Backgrounds of Empathy\nEmpathy, defined as sharing othersâ emotions and experiences, is a multifaceted concept encompassing cognitive and emotional aspects (Neff, 2003; Anderson and Keltner, 2002; De Vignemont and Singer, 2006; Hall and Schwartz, 2019; Zaki, 2019). Cognitive empathy involves understanding othersâ emotions and perspectives, linked to abilities such as mentalizing and narrative imagination (Eisenberg, 2014). It requires an in-depth cognitive appraisal of the situation, considering factors like pleasantness, control, and certainty of the outcome (Lazarus, 1991; Wondra and (emotional) 2015). Affective Ellsworth, empathy allows to experience individuals othersâ emotions, while motivational empathy, a newer concept, embodies the desire to alleviate othersâ emotional distress (Zaki, 2019).\n# 2.2. Empathetic Communication in Text\nNatural Language Processing (NLP) has been increasingly developing conversational agents, or chatbots, across various professional domains. These include mental healthcare for victims of crime (Ahn et al., 2020), individuals on the autism spectrum (Diehl et al., 2012), and those suffering from anxiety disorders (Rasouli et al., 2022).",
"title": "Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models",
"year": 2023
} | [
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] |
2311.01343 | 5 | Several preliminary studies have been conducted to investigate the adaptation of LLMs for recommendation systems [19â22]. Typ- ically, these methods can be summarized into two steps: 1) First, instead of representing users/items with continuous ID embeddings, relevant information necessary for reasoning with user interests and generating recommendations, i.e., target user, interacted items, user/item features, and candidate items, are converted into a nat- ural language-based prompt. 2) Then, the prompt is used to query the LLM, where information relevant to recommendations (e.g., whether the user will interact with an item or not) is retrieved from the textual output of the LLM to generate recommendations. The above procedure can be performed in a zero-shot manner [23â26], where the recommendation decisions are obtained directly from the pretrained LLM (e.g., we input all relevant information regarding a user and an item into the chatbox of ChatGPT and ask if the user will interact with the item), or if groundtruths are available, the pretrained LLMs can also be finetuned, such that RS-specific knowledge can be updated into the pretrained model [20, 27â29]. Although progress has been achieved by | 2311.01343#5 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
{
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 5,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "Several preliminary studies have been conducted to investigate the adaptation of LLMs for recommendation systems [19â22]. Typ- ically, these methods can be summarized into two steps: 1) First, instead of representing users/items with continuous ID embeddings, relevant information necessary for reasoning with user interests and generating recommendations, i.e., target user, interacted items, user/item features, and candidate items, are converted into a nat- ural language-based prompt. 2) Then, the prompt is used to query the LLM, where information relevant to recommendations (e.g., whether the user will interact with an item or not) is retrieved from the textual output of the LLM to generate recommendations. The above procedure can be performed in a zero-shot manner [23â26], where the recommendation decisions are obtained directly from the pretrained LLM (e.g., we input all relevant information regarding a user and an item into the chatbox of ChatGPT and ask if the user will interact with the item), or if groundtruths are available, the pretrained LLMs can also be finetuned, such that RS-specific knowledge can be updated into the pretrained model [20, 27â29]. Although progress has been achieved by",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} | [
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2311.04915 | 5 | Recently, chatbots designed for psychotherapy (e.g., CBT) have shown promising results in assisting the long-term treatment of anxiety and depression (Nwosu et al., 2022). However, current AI-generated responses appear generic and less authentic, making personalized responses a significant challenge. Empathetic reasoning is crucial for these systems, leading to ongoing efforts to enhance their empathetic expression incorporating human-like traits (Roller et al., 2021).
# 2.3. Computational Approach to Empathy
Past research in psychotherapy has primarily focused on empathy based on the analysis of nonverbal cues, such as body language and facial expressions, often requiring manual coding of empathetic responses (Scherer et al., 2001; American Psychiatric Association et al., 1994; Ekman and Friesen, 1971).
Recent advances in artificial intelligence have shifted towards a computational approach, where empathy is predicted from a text corpus and quantified through the labeling of emotions (Rashkin et al., 2019) and distress (Buechel et al., 2018). While most studies have traditionally concentrated on the clientâs capacity for the empathy, counselor is increasingly recognized as critical to successful therapy outcomes (Truax and Carkhuff, 2007). This aspect of expressed empathy is particularly relevant to our approach, where we aim to use LLMs to reflect their understanding of the clientâs needs accurately.
# 2.4. Reasoning in Large Language Models | 2311.04915#5 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI. | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | [
{
"id": "2302.13971"
},
{
"id": "2305.10601"
},
{
"id": "2212.14402"
},
{
"id": "2205.11916"
},
{
"id": "2108.07258"
},
{
"id": "2209.08141"
},
{
"id": "2201.11903"
},
{
"id": "2306.08997"
},
{
"id": "2303.13375"
},
{
"id": "1808.10399"
}
] | {
"authors": "Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn",
"chunk_id": 5,
"doc_id": "2311.04915",
"primary_category": "cs.CL",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.04915",
"summary": "We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psychotherapy to induce Large Language Models (LLMs) to\nreason about human emotional states. This method is inspired by various\npsychotherapy approaches including Cognitive Behavioral Therapy (CBT),\nDialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality\nTherapy (RT), each leading to different patterns of interpreting clients'\nmental states. LLMs without reasoning generated predominantly exploratory\nresponses. However, when LLMs used CoE reasoning, we found a more comprehensive\nrange of empathetic responses aligned with the different reasoning patterns of\neach psychotherapy model. The CBT based CoE resulted in the most balanced\ngeneration of empathetic responses. The findings underscore the importance of\nunderstanding the emotional context and how it affects human and AI\ncommunication. Our research contributes to understanding how psychotherapeutic\nmodels can be incorporated into LLMs, facilitating the development of\ncontext-specific, safer, and empathetic AI.",
"text": "Recently, chatbots designed for psychotherapy (e.g., CBT) have shown promising results in assisting the long-term treatment of anxiety and depression (Nwosu et al., 2022). However, current AI-generated responses appear generic and less authentic, making personalized responses a significant challenge. Empathetic reasoning is crucial for these systems, leading to ongoing efforts to enhance their empathetic expression incorporating human-like traits (Roller et al., 2021).\n# 2.3. Computational Approach to Empathy\nPast research in psychotherapy has primarily focused on empathy based on the analysis of nonverbal cues, such as body language and facial expressions, often requiring manual coding of empathetic responses (Scherer et al., 2001; American Psychiatric Association et al., 1994; Ekman and Friesen, 1971).\nRecent advances in artificial intelligence have shifted towards a computational approach, where empathy is predicted from a text corpus and quantified through the labeling of emotions (Rashkin et al., 2019) and distress (Buechel et al., 2018). While most studies have traditionally concentrated on the clientâs capacity for the empathy, counselor is increasingly recognized as critical to successful therapy outcomes (Truax and Carkhuff, 2007). This aspect of expressed empathy is particularly relevant to our approach, where we aim to use LLMs to reflect their understanding of the clientâs needs accurately.\n# 2.4. Reasoning in Large Language Models",
"title": "Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models",
"year": 2023
} | [
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2311.01343 | 6 | the pretrained LLMs can also be finetuned, such that RS-specific knowledge can be updated into the pretrained model [20, 27â29]. Although progress has been achieved by these pioneer works, some fundamental dichotomies between natural language process- ing (NLP) and recommendation still remain to be addressed. One main challenge is the gap between natural language and user/item semantics. Generally, there are two strategies to represent user/item | 2311.01343#6 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
{
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 6,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "the pretrained LLMs can also be finetuned, such that RS-specific knowledge can be updated into the pretrained model [20, 27â29]. Although progress has been achieved by these pioneer works, some fundamental dichotomies between natural language process- ing (NLP) and recommendation still remain to be addressed. One main challenge is the gap between natural language and user/item semantics. Generally, there are two strategies to represent user/item",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} | [
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] |
2311.04915 | 6 | # 2.4. Reasoning in Large Language Models
Recently, CoT has shown effective in eliciting the reasoning process of the LLMs (Wei et al., 2022; Prystawski et al., 2022; Yao et al., 2023; Kojima et al., 2022). CoT prompting in previous research has included reasoning steps within the prompt instruction for zero- or one- shot learning of LLMs during text generation
CBT-CoE Goal Cognitive reframing Reasoning Tackling negative thought patterns Prompt conditions DBT-CoE PCT-CoE Emotion regulation Addressing emotional dysregulation Self- understanding Enhancing self-awareness RT-CoE Problem-focused coping Identifying cause of the dissatisfaction
Table 1: Comparison of goals and reasoning style in different psychotherapy based CoEs.
(Kojima et al., 2022). This model has improved the performance of problem-solving (Kojima et al., understanding or metaphor (Prystawski et al., 2022), offering new insights and suggesting possibilities for generative models to be used in many other domains.
2011; Knutson and Koch, 2022), and Reality Therapy (RT; Wubbolding et al., 2017)2. Except these promptsâ for instructions were designed the to reflect therapistsâ reasoning process in their respective counseling models.
# 3. The Present Study | 2311.04915#6 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI. | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | [
{
"id": "2302.13971"
},
{
"id": "2305.10601"
},
{
"id": "2212.14402"
},
{
"id": "2205.11916"
},
{
"id": "2108.07258"
},
{
"id": "2209.08141"
},
{
"id": "2201.11903"
},
{
"id": "2306.08997"
},
{
"id": "2303.13375"
},
{
"id": "1808.10399"
}
] | {
"authors": "Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn",
"chunk_id": 6,
"doc_id": "2311.04915",
"primary_category": "cs.CL",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.04915",
"summary": "We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psychotherapy to induce Large Language Models (LLMs) to\nreason about human emotional states. This method is inspired by various\npsychotherapy approaches including Cognitive Behavioral Therapy (CBT),\nDialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality\nTherapy (RT), each leading to different patterns of interpreting clients'\nmental states. LLMs without reasoning generated predominantly exploratory\nresponses. However, when LLMs used CoE reasoning, we found a more comprehensive\nrange of empathetic responses aligned with the different reasoning patterns of\neach psychotherapy model. The CBT based CoE resulted in the most balanced\ngeneration of empathetic responses. The findings underscore the importance of\nunderstanding the emotional context and how it affects human and AI\ncommunication. Our research contributes to understanding how psychotherapeutic\nmodels can be incorporated into LLMs, facilitating the development of\ncontext-specific, safer, and empathetic AI.",
"text": "# 2.4. Reasoning in Large Language Models\nRecently, CoT has shown effective in eliciting the reasoning process of the LLMs (Wei et al., 2022; Prystawski et al., 2022; Yao et al., 2023; Kojima et al., 2022). CoT prompting in previous research has included reasoning steps within the prompt instruction for zero- or one- shot learning of LLMs during text generation\nCBT-CoE Goal Cognitive reframing Reasoning Tackling negative thought patterns Prompt conditions DBT-CoE PCT-CoE Emotion regulation Addressing emotional dysregulation Self- understanding Enhancing self-awareness RT-CoE Problem-focused coping Identifying cause of the dissatisfaction\nTable 1: Comparison of goals and reasoning style in different psychotherapy based CoEs.\n(Kojima et al., 2022). This model has improved the performance of problem-solving (Kojima et al., understanding or metaphor (Prystawski et al., 2022), offering new insights and suggesting possibilities for generative models to be used in many other domains.\n2011; Knutson and Koch, 2022), and Reality Therapy (RT; Wubbolding et al., 2017)2. Except these promptsâ for instructions were designed the to reflect therapistsâ reasoning process in their respective counseling models.\n# 3. The Present Study",
"title": "Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models",
"year": 2023
} | [
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] |
2311.04915 | 7 | # 3. The Present Study
We investigated whether eliciting empathetic reasoning in LLMs leads to natural responses. Therefore, we developed CoE prompting to reason emotion and situational factors that could help the model to accurately infer the clientâs emotional experience in mental healthcare and thus choose the most appropriate and context-aware empathetic strategy to communicate.
Models in each prompting condition were tested in zero-shot, with only instructions on which option to choose per class: empathetic strategy (emotional reaction, exploration, and interpretation) and communication level (no expression, weak, and strong) (Sharma et al., 2020). The common reasoning steps involved in each CoE condition were: (1) Identify any word that represents the clientâs emotion, and individual/situational (2) Understand factors that may have led to the expression in the clientâs message.
# 4. Methods
# 4.1. Language Model
# 5. Experiments
We used GPT-3.5 API from OpenAI 1 for system setup. The model (âtext-davinci-003â) temperature was set to 0.9. The top p parameter was set to 1 for nucleus sampling to reduce the randomness of the output (The frequency penalty = 0 and the presence penalty = 0.6).
# 4.2. Chain-of-Empathy Reasoning | 2311.04915#7 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI. | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | [
{
"id": "2302.13971"
},
{
"id": "2305.10601"
},
{
"id": "2212.14402"
},
{
"id": "2205.11916"
},
{
"id": "2108.07258"
},
{
"id": "2209.08141"
},
{
"id": "2201.11903"
},
{
"id": "2306.08997"
},
{
"id": "2303.13375"
},
{
"id": "1808.10399"
}
] | {
"authors": "Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn",
"chunk_id": 7,
"doc_id": "2311.04915",
"primary_category": "cs.CL",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.04915",
"summary": "We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psychotherapy to induce Large Language Models (LLMs) to\nreason about human emotional states. This method is inspired by various\npsychotherapy approaches including Cognitive Behavioral Therapy (CBT),\nDialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality\nTherapy (RT), each leading to different patterns of interpreting clients'\nmental states. LLMs without reasoning generated predominantly exploratory\nresponses. However, when LLMs used CoE reasoning, we found a more comprehensive\nrange of empathetic responses aligned with the different reasoning patterns of\neach psychotherapy model. The CBT based CoE resulted in the most balanced\ngeneration of empathetic responses. The findings underscore the importance of\nunderstanding the emotional context and how it affects human and AI\ncommunication. Our research contributes to understanding how psychotherapeutic\nmodels can be incorporated into LLMs, facilitating the development of\ncontext-specific, safer, and empathetic AI.",
"text": "# 3. The Present Study\nWe investigated whether eliciting empathetic reasoning in LLMs leads to natural responses. Therefore, we developed CoE prompting to reason emotion and situational factors that could help the model to accurately infer the clientâs emotional experience in mental healthcare and thus choose the most appropriate and context-aware empathetic strategy to communicate.\nModels in each prompting condition were tested in zero-shot, with only instructions on which option to choose per class: empathetic strategy (emotional reaction, exploration, and interpretation) and communication level (no expression, weak, and strong) (Sharma et al., 2020). The common reasoning steps involved in each CoE condition were: (1) Identify any word that represents the clientâs emotion, and individual/situational (2) Understand factors that may have led to the expression in the clientâs message.\n# 4. Methods\n# 4.1. Language Model\n# 5. Experiments\nWe used GPT-3.5 API from OpenAI 1 for system setup. The model (âtext-davinci-003â) temperature was set to 0.9. The top p parameter was set to 1 for nucleus sampling to reduce the randomness of the output (The frequency penalty = 0 and the presence penalty = 0.6).\n# 4.2. Chain-of-Empathy Reasoning",
"title": "Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models",
"year": 2023
} | [
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2311.01343 | 8 | in an LLM-based RS. One strategy is pseudo-ID-based method, where an ID-like word (e.g., "user_ð" or "item_ð") is used to rep- resent the ðth user and ðth item [20]. However, since the vocabu- lary of most LLM contains number-tokens up to two digits, when tokenized, the pseudo ID breaks down into atomic tokens, e.g., "user_4332" into ["user", "_", "43", "32"], where spurious correlations can be introduced for irrelevant users/items (e.g., "user_4332" with "user_43" and "user_32"). In contrast, description-based methods use semantically meaningful descriptions to index users/items, such as item titles [19, 24] or a small amount of newly-introduced tokens assigned to different user/items based on their content similarity [30]. However, description-based methods introduce a strong induc- tive bias on user-item semantic similarity, which may not faithfully capture the true semantics. Introducing user/item ID tokens, un- fortunately, is generally considered infeasible for LLMs, | 2311.01343#8 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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"id": "2303.18223"
},
{
"id": "2302.03735"
},
{
"id": "2305.00447"
},
{
"id": "2305.08845"
},
{
"id": "2104.08691"
},
{
"id": "2308.10837"
},
{
"id": "2305.06569"
}
] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 8,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "in an LLM-based RS. One strategy is pseudo-ID-based method, where an ID-like word (e.g., \"user_ð\" or \"item_ð\") is used to rep- resent the ðth user and ðth item [20]. However, since the vocabu- lary of most LLM contains number-tokens up to two digits, when tokenized, the pseudo ID breaks down into atomic tokens, e.g., \"user_4332\" into [\"user\", \"_\", \"43\", \"32\"], where spurious correlations can be introduced for irrelevant users/items (e.g., \"user_4332\" with \"user_43\" and \"user_32\"). In contrast, description-based methods use semantically meaningful descriptions to index users/items, such as item titles [19, 24] or a small amount of newly-introduced tokens assigned to different user/items based on their content similarity [30]. However, description-based methods introduce a strong induc- tive bias on user-item semantic similarity, which may not faithfully capture the true semantics. Introducing user/item ID tokens, un- fortunately, is generally considered infeasible for LLMs,",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} | [
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] |
2311.04915 | 8 | # 4.2. Chain-of-Empathy Reasoning
Table 1 and Figure 1 show four unique prompts with CoE in addition to the base condition (no reasoning): Cognitive-Behavioral Therapy (CBT; Beck, 1979; Kaczkurkin and Foa, 2022; Hofmann et al., 2010), Dialectical Behavior Therapy (DBT; Linehan, 1987), Person- Centered Therapy (PCT; Cooper and McLeod,
We to generate appropriate responses to the posts of seekers seeking advice on Reddit and predict the best suitable empathetic strategy. For the ground- truth label of each empathetic strategy class, we used the EPITOME 3 , crowdsourced Reddit posts of mental health, with an average inter- annotator agreement reported as above 0.68 (Sharma et al., 2020). The dataset comprised pairs of help-seeking posts and responding posts. Each pair was labeled based on (1) the type of expressed âempathy mechanismâ (i.e.,
1 https://openai.com/ 2 We want to emphasize that these descriptions are not exhaustive representations of the goals of each psychotherapy. These goals and reasoning strategies have been specifically modified for LLM prompting
and do not reflect the entire interaction between clinical/counseling psychologists and clients. 3 https://github.com/behavioral-data/ Empathy- Mental-Health | 2311.04915#8 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI. | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | [
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"authors": "Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn",
"chunk_id": 8,
"doc_id": "2311.04915",
"primary_category": "cs.CL",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.04915",
"summary": "We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psychotherapy to induce Large Language Models (LLMs) to\nreason about human emotional states. This method is inspired by various\npsychotherapy approaches including Cognitive Behavioral Therapy (CBT),\nDialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality\nTherapy (RT), each leading to different patterns of interpreting clients'\nmental states. LLMs without reasoning generated predominantly exploratory\nresponses. However, when LLMs used CoE reasoning, we found a more comprehensive\nrange of empathetic responses aligned with the different reasoning patterns of\neach psychotherapy model. The CBT based CoE resulted in the most balanced\ngeneration of empathetic responses. The findings underscore the importance of\nunderstanding the emotional context and how it affects human and AI\ncommunication. Our research contributes to understanding how psychotherapeutic\nmodels can be incorporated into LLMs, facilitating the development of\ncontext-specific, safer, and empathetic AI.",
"text": "# 4.2. Chain-of-Empathy Reasoning\nTable 1 and Figure 1 show four unique prompts with CoE in addition to the base condition (no reasoning): Cognitive-Behavioral Therapy (CBT; Beck, 1979; Kaczkurkin and Foa, 2022; Hofmann et al., 2010), Dialectical Behavior Therapy (DBT; Linehan, 1987), Person- Centered Therapy (PCT; Cooper and McLeod,\nWe to generate appropriate responses to the posts of seekers seeking advice on Reddit and predict the best suitable empathetic strategy. For the ground- truth label of each empathetic strategy class, we used the EPITOME 3 , crowdsourced Reddit posts of mental health, with an average inter- annotator agreement reported as above 0.68 (Sharma et al., 2020). The dataset comprised pairs of help-seeking posts and responding posts. Each pair was labeled based on (1) the type of expressed âempathy mechanismâ (i.e.,\n1 https://openai.com/ 2 We want to emphasize that these descriptions are not exhaustive representations of the goals of each psychotherapy. These goals and reasoning strategies have been specifically modified for LLM prompting\nand do not reflect the entire interaction between clinical/counseling psychologists and clients. 3 https://github.com/behavioral-data/ Empathy- Mental-Health",
"title": "Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models",
"year": 2023
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2311.01343 | 9 | which may not faithfully capture the true semantics. Introducing user/item ID tokens, un- fortunately, is generally considered infeasible for LLMs, as directly conducting language modeling on sequences with heterogeneous tokens can be ineffective and unstable, especially when the vocabu- lary of most LLMs is diluted (e.g., â¼ 50k for GPT, and â¼ 30k for T5) by a large number of randomly initialized user/item embeddings. Even if user/item ID token embeddings can be effectively learned via language modeling, another challenge that hinders effective collaborative filtering with LLMs is that, since the order of inter- actions usually does not matter for direct recommendations while human language naturally has an order, spurious temporal cor- relation can be introduced for items placed in different positions when transforming the user historical interactions into textual sen- tences. Furthermore, for content modeling, since pretrained LLMs are not recommendation-oriented, they can easily capture noise in the user/item textual features irrelevant to the recommendation purpose. Finally, since LLMs generate the next token in an autore- gressive manner, recommending multiple items can be inefficient. For both pseudo-ID-based | 2311.01343#9 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 9,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "which may not faithfully capture the true semantics. Introducing user/item ID tokens, un- fortunately, is generally considered infeasible for LLMs, as directly conducting language modeling on sequences with heterogeneous tokens can be ineffective and unstable, especially when the vocabu- lary of most LLMs is diluted (e.g., â¼ 50k for GPT, and â¼ 30k for T5) by a large number of randomly initialized user/item embeddings. Even if user/item ID token embeddings can be effectively learned via language modeling, another challenge that hinders effective collaborative filtering with LLMs is that, since the order of inter- actions usually does not matter for direct recommendations while human language naturally has an order, spurious temporal cor- relation can be introduced for items placed in different positions when transforming the user historical interactions into textual sen- tences. Furthermore, for content modeling, since pretrained LLMs are not recommendation-oriented, they can easily capture noise in the user/item textual features irrelevant to the recommendation purpose. Finally, since LLMs generate the next token in an autore- gressive manner, recommending multiple items can be inefficient. For both pseudo-ID-based",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
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2311.04915 | 9 | and do not reflect the entire interaction between clinical/counseling psychologists and clients. 3 https://github.com/behavioral-data/ Empathy- Mental-Health
Acc Emotional Reaction Interpretation Exploration Base 0.340 Prec. 0.467 Recall 0.185 F1 0.27 Prec. 0 Recall 0 F1 0 Prec. 0.327 Recall F1 0.866 0.475 CBT-CoE 0.319 0.463 0.165 0.244 0.293 0.260 0.276 0.303 0.543 0.389 DBT-CoE 0.334 0.392 0.372 0.382 0.291 0.060 0.100 0.309 0.582 0.404 PCT-CoE 0.336 0.399 0.243 0.302 0.333 0.016 0.031 0.319 0.757 0.449 RT-CoE 0.336 0.407 0.308 0.350 0.354 0.044 0.079 0.309 0.664 0.420
Table 2: Model performance in empathetic strategy classification task by CoE prompting conditions. *Prec. = Precision | 2311.04915#9 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI. | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | [
{
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},
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] | {
"authors": "Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn",
"chunk_id": 9,
"doc_id": "2311.04915",
"primary_category": "cs.CL",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.04915",
"summary": "We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psychotherapy to induce Large Language Models (LLMs) to\nreason about human emotional states. This method is inspired by various\npsychotherapy approaches including Cognitive Behavioral Therapy (CBT),\nDialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality\nTherapy (RT), each leading to different patterns of interpreting clients'\nmental states. LLMs without reasoning generated predominantly exploratory\nresponses. However, when LLMs used CoE reasoning, we found a more comprehensive\nrange of empathetic responses aligned with the different reasoning patterns of\neach psychotherapy model. The CBT based CoE resulted in the most balanced\ngeneration of empathetic responses. The findings underscore the importance of\nunderstanding the emotional context and how it affects human and AI\ncommunication. Our research contributes to understanding how psychotherapeutic\nmodels can be incorporated into LLMs, facilitating the development of\ncontext-specific, safer, and empathetic AI.",
"text": "and do not reflect the entire interaction between clinical/counseling psychologists and clients. 3 https://github.com/behavioral-data/ Empathy- Mental-Health\nAcc Emotional Reaction Interpretation Exploration Base 0.340 Prec. 0.467 Recall 0.185 F1 0.27 Prec. 0 Recall 0 F1 0 Prec. 0.327 Recall F1 0.866 0.475 CBT-CoE 0.319 0.463 0.165 0.244 0.293 0.260 0.276 0.303 0.543 0.389 DBT-CoE 0.334 0.392 0.372 0.382 0.291 0.060 0.100 0.309 0.582 0.404 PCT-CoE 0.336 0.399 0.243 0.302 0.333 0.016 0.031 0.319 0.757 0.449 RT-CoE 0.336 0.407 0.308 0.350 0.354 0.044 0.079 0.309 0.664 0.420\nTable 2: Model performance in empathetic strategy classification task by CoE prompting conditions. *Prec. = Precision",
"title": "Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models",
"year": 2023
} | [
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2311.04915 | 10 | Table 2: Model performance in empathetic strategy classification task by CoE prompting conditions. *Prec. = Precision
empathy strategy) and (2) the presence and âlevelâ of each expressed empathy (i.e., communication strength). The three empathy reaction, strategies exploration, with corresponding levels of 0, 1, and 2. Pairs labeled as level 0, indicating no expression of empathy, were excluded. The number of pairs for each strategy was as follows: âemotion reactionâ=1,047, and âinterpretationâ=1,436. We randomly sampled 500 pairs in each emotional reaction and interpretation data to balance the number of pairs between strategies. Each strategyâs final number of pairs was emotional reaction=500, exploration=480, and interpretation=500.
# 5.1. Model Performances
Table 2 and Figure 2 show the performance of the empathetic strategy classification of LLMs with each CoE prompt, measured in terms of precision, recall, F1 score, and accuracy. Upon generating a response, each model with CoE prompts predicted which empathy strategy is most suitable for each seekerâs post among the three strategies. We the predicted empathy strategy with the ground truth calculated strategy prediction accuracy. | 2311.04915#10 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI. | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | [
{
"id": "2302.13971"
},
{
"id": "2305.10601"
},
{
"id": "2212.14402"
},
{
"id": "2205.11916"
},
{
"id": "2108.07258"
},
{
"id": "2209.08141"
},
{
"id": "2201.11903"
},
{
"id": "2306.08997"
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"id": "2303.13375"
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}
] | {
"authors": "Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn",
"chunk_id": 10,
"doc_id": "2311.04915",
"primary_category": "cs.CL",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.04915",
"summary": "We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psychotherapy to induce Large Language Models (LLMs) to\nreason about human emotional states. This method is inspired by various\npsychotherapy approaches including Cognitive Behavioral Therapy (CBT),\nDialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality\nTherapy (RT), each leading to different patterns of interpreting clients'\nmental states. LLMs without reasoning generated predominantly exploratory\nresponses. However, when LLMs used CoE reasoning, we found a more comprehensive\nrange of empathetic responses aligned with the different reasoning patterns of\neach psychotherapy model. The CBT based CoE resulted in the most balanced\ngeneration of empathetic responses. The findings underscore the importance of\nunderstanding the emotional context and how it affects human and AI\ncommunication. Our research contributes to understanding how psychotherapeutic\nmodels can be incorporated into LLMs, facilitating the development of\ncontext-specific, safer, and empathetic AI.",
"text": "Table 2: Model performance in empathetic strategy classification task by CoE prompting conditions. *Prec. = Precision\nempathy strategy) and (2) the presence and âlevelâ of each expressed empathy (i.e., communication strength). The three empathy reaction, strategies exploration, with corresponding levels of 0, 1, and 2. Pairs labeled as level 0, indicating no expression of empathy, were excluded. The number of pairs for each strategy was as follows: âemotion reactionâ=1,047, and âinterpretationâ=1,436. We randomly sampled 500 pairs in each emotional reaction and interpretation data to balance the number of pairs between strategies. Each strategyâs final number of pairs was emotional reaction=500, exploration=480, and interpretation=500.\n# 5.1. Model Performances\nTable 2 and Figure 2 show the performance of the empathetic strategy classification of LLMs with each CoE prompt, measured in terms of precision, recall, F1 score, and accuracy. Upon generating a response, each model with CoE prompts predicted which empathy strategy is most suitable for each seekerâs post among the three strategies. We the predicted empathy strategy with the ground truth calculated strategy prediction accuracy.",
"title": "Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models",
"year": 2023
} | [
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2311.01343 | 11 | To address the above challenges, we present CLLM4Rec, the first method that tightly combines the ID paradigm of RS with the LLM-based paradigm to address the semantic gap. We first extend the vocabulary of pretrained LLMs with user/item ID tokens to faith- fully model the user/item collaborative/content semantics, where the embeddings are learned in two stages. The pretraining stage consists of mutually-regularized collaborative and content LLMs that learn user/item token embeddings via language modeling on RS-specific corpora established from user/item interactions and tex- tual features. Specifically, a novel "soft+hard" prompting strategy is proposed for effective language modeling on documents with heterogeneous tokens, where each document is decomposed into a prompt consisting of user/item (soft [31]) and vocab (hard) tokens that describe the contexts and a main text consisting of homoge- neous item tokens (i.e., interaction history) or vocab tokens (i.e., user/item textual features), respectively. Through this strategy, the prediction heads for the two LLMs can focus exclusively on collab- orative and content information, and the stability and effectiveness of language modeling can be substantially enhanced. In addition, a stochastic reordering strategy is proposed for the collaborative LLM to ignore the order of item tokens without negative influence on the vocab tokens. Finally, we propose a novel recommendation-oriented | 2311.01343#11 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
{
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 11,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "To address the above challenges, we present CLLM4Rec, the first method that tightly combines the ID paradigm of RS with the LLM-based paradigm to address the semantic gap. We first extend the vocabulary of pretrained LLMs with user/item ID tokens to faith- fully model the user/item collaborative/content semantics, where the embeddings are learned in two stages. The pretraining stage consists of mutually-regularized collaborative and content LLMs that learn user/item token embeddings via language modeling on RS-specific corpora established from user/item interactions and tex- tual features. Specifically, a novel \"soft+hard\" prompting strategy is proposed for effective language modeling on documents with heterogeneous tokens, where each document is decomposed into a prompt consisting of user/item (soft [31]) and vocab (hard) tokens that describe the contexts and a main text consisting of homoge- neous item tokens (i.e., interaction history) or vocab tokens (i.e., user/item textual features), respectively. Through this strategy, the prediction heads for the two LLMs can focus exclusively on collab- orative and content information, and the stability and effectiveness of language modeling can be substantially enhanced. In addition, a stochastic reordering strategy is proposed for the collaborative LLM to ignore the order of item tokens without negative influence on the vocab tokens. Finally, we propose a novel recommendation-oriented",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} | [
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2311.04915 | 11 | retrieval (e.g., âNo Empathy Strategyâ). In addition, they sometimes predicted new strategies which did not fall into any of the predefined three strategies (e.g., âReflection,â âValidation: clientâs acknowledging feelings and experiences,â and âApproval: expressing approval or positive reinforcement to the clientâ).
# 6. Qualitative Evaluations
The LLM generally generated courteous and comprehensive responses. While many human peer supporters often provided brief comments and shared personal opinions or give advice, the CoE LLM mostly responded with at least two empathetic strategies and frequently suggested seeking professional help. The model tended to initiate responses by interpreting usersâ current state and subsequent advice or exploring potential options. For example, when a distressed seeker could not control her anxiety after a violent fight between her parents, DBT- responded with multiple CoE prompt empathetic strategies, âIâm so sorry you had to witness that. Itâs understandable that youâre
Outputs with errors in the predicted strategy names were excluded from the analysis. Most of these errors resulted from the nature of LLM as a generative model, which behaves differently from traditional supervised learning models for classification tasks. Despite explicit instructions the models occasionally generated ânoiseâ output and predicted strategies that were not among the provided options. These errors include responses of failed predictions or response | 2311.04915#11 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI. | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | [
{
"id": "2302.13971"
},
{
"id": "2305.10601"
},
{
"id": "2212.14402"
},
{
"id": "2205.11916"
},
{
"id": "2108.07258"
},
{
"id": "2209.08141"
},
{
"id": "2201.11903"
},
{
"id": "2306.08997"
},
{
"id": "2303.13375"
},
{
"id": "1808.10399"
}
] | {
"authors": "Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn",
"chunk_id": 11,
"doc_id": "2311.04915",
"primary_category": "cs.CL",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.04915",
"summary": "We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psychotherapy to induce Large Language Models (LLMs) to\nreason about human emotional states. This method is inspired by various\npsychotherapy approaches including Cognitive Behavioral Therapy (CBT),\nDialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality\nTherapy (RT), each leading to different patterns of interpreting clients'\nmental states. LLMs without reasoning generated predominantly exploratory\nresponses. However, when LLMs used CoE reasoning, we found a more comprehensive\nrange of empathetic responses aligned with the different reasoning patterns of\neach psychotherapy model. The CBT based CoE resulted in the most balanced\ngeneration of empathetic responses. The findings underscore the importance of\nunderstanding the emotional context and how it affects human and AI\ncommunication. Our research contributes to understanding how psychotherapeutic\nmodels can be incorporated into LLMs, facilitating the development of\ncontext-specific, safer, and empathetic AI.",
"text": "retrieval (e.g., âNo Empathy Strategyâ). In addition, they sometimes predicted new strategies which did not fall into any of the predefined three strategies (e.g., âReflection,â âValidation: clientâs acknowledging feelings and experiences,â and âApproval: expressing approval or positive reinforcement to the clientâ).\n# 6. Qualitative Evaluations\nThe LLM generally generated courteous and comprehensive responses. While many human peer supporters often provided brief comments and shared personal opinions or give advice, the CoE LLM mostly responded with at least two empathetic strategies and frequently suggested seeking professional help. The model tended to initiate responses by interpreting usersâ current state and subsequent advice or exploring potential options. For example, when a distressed seeker could not control her anxiety after a violent fight between her parents, DBT- responded with multiple CoE prompt empathetic strategies, âIâm so sorry you had to witness that. Itâs understandable that youâre\nOutputs with errors in the predicted strategy names were excluded from the analysis. Most of these errors resulted from the nature of LLM as a generative model, which behaves differently from traditional supervised learning models for classification tasks. Despite explicit instructions the models occasionally generated ânoiseâ output and predicted strategies that were not among the provided options. These errors include responses of failed predictions or response",
"title": "Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models",
"year": 2023
} | [
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] |
2311.01343 | 12 | Yaochen Zhuâ,1, Liang Wu2, Qi Guo2, Liangjie Hong2, Jundong Li1
finetuning strategy for CLLM4Rec, where an item prediction head with multinomial likelihood is added to the pretrained collabora- tive LLM backbone to predict hold-out items based on soft+hard prompts established from masked usersâ interaction history, where recommendations of multiple items can be generated efficiently. The contribution of this paper can be concretely summarized as: | 2311.01343#12 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
{
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 12,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "Yaochen Zhuâ,1, Liang Wu2, Qi Guo2, Liangjie Hong2, Jundong Li1\nfinetuning strategy for CLLM4Rec, where an item prediction head with multinomial likelihood is added to the pretrained collabora- tive LLM backbone to predict hold-out items based on soft+hard prompts established from masked usersâ interaction history, where recommendations of multiple items can be generated efficiently. The contribution of this paper can be concretely summarized as:",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} | [
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] |
2311.04915 | 12 | Base 10 og 08 06 06 Fl-score Fl-score 04 04 0.2 emotion reactions interpretation Empathy Strategy 0.0 0.0 exploration PCT CoE 1.0 og 08 0.6 06 Fl-score Fl-score 04 o4 02 emotion_reactions interpretation Empathy Strategy 0.0 0.0 exploration CBT CoE emotion reactions interpretation Empathy Strategy RT CoE DBT CoE 10 08 06 Fl-score 04 emotion reactions interpretation Empathy Strategy 0.0 exploration exploration emotion_reactions interpretation exploration Empathy Strategy
Figure 2: Empathic expression strategy classification accuracy per prompt conditions. Compared to Base condition, CBT-CoE provided the balanced set of each empathy expression but less emotional reaction than other CoEs.
feeling overwhelmed and scared right now. Itâs not okay for anyone to threaten or hurt another person, and itâs not your fault. How can I support you right now?â. This contradicts the original human response in benchmark data: âEverything is wrong with people.â
# 7. Conclusions | 2311.04915#12 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI. | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | [
{
"id": "2302.13971"
},
{
"id": "2305.10601"
},
{
"id": "2212.14402"
},
{
"id": "2205.11916"
},
{
"id": "2108.07258"
},
{
"id": "2209.08141"
},
{
"id": "2201.11903"
},
{
"id": "2306.08997"
},
{
"id": "2303.13375"
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{
"id": "1808.10399"
}
] | {
"authors": "Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn",
"chunk_id": 12,
"doc_id": "2311.04915",
"primary_category": "cs.CL",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.04915",
"summary": "We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psychotherapy to induce Large Language Models (LLMs) to\nreason about human emotional states. This method is inspired by various\npsychotherapy approaches including Cognitive Behavioral Therapy (CBT),\nDialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality\nTherapy (RT), each leading to different patterns of interpreting clients'\nmental states. LLMs without reasoning generated predominantly exploratory\nresponses. However, when LLMs used CoE reasoning, we found a more comprehensive\nrange of empathetic responses aligned with the different reasoning patterns of\neach psychotherapy model. The CBT based CoE resulted in the most balanced\ngeneration of empathetic responses. The findings underscore the importance of\nunderstanding the emotional context and how it affects human and AI\ncommunication. Our research contributes to understanding how psychotherapeutic\nmodels can be incorporated into LLMs, facilitating the development of\ncontext-specific, safer, and empathetic AI.",
"text": "Base 10 og 08 06 06 Fl-score Fl-score 04 04 0.2 emotion reactions interpretation Empathy Strategy 0.0 0.0 exploration PCT CoE 1.0 og 08 0.6 06 Fl-score Fl-score 04 o4 02 emotion_reactions interpretation Empathy Strategy 0.0 0.0 exploration CBT CoE emotion reactions interpretation Empathy Strategy RT CoE DBT CoE 10 08 06 Fl-score 04 emotion reactions interpretation Empathy Strategy 0.0 exploration exploration emotion_reactions interpretation exploration Empathy Strategy\nFigure 2: Empathic expression strategy classification accuracy per prompt conditions. Compared to Base condition, CBT-CoE provided the balanced set of each empathy expression but less emotional reaction than other CoEs.\nfeeling overwhelmed and scared right now. Itâs not okay for anyone to threaten or hurt another person, and itâs not your fault. How can I support you right now?â. This contradicts the original human response in benchmark data: âEverything is wrong with people.â\n# 7. Conclusions",
"title": "Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models",
"year": 2023
} | [
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] |
2311.01343 | 13 | We present CLLM4Rec, the first framework that tightly couples the ID paradigm and LLM paradigm of RS, where encoded knowledge and reasoning ability of LLMs can be fully utilized, while user/item ID token embeddings aligned to the vocab space can well capture intrinsic user interests and item properties. ⢠A novel soft+hard prompting strategy is proposed to pretrain the LLMs on sequences of heterogeneous tokens describing user historical interactions and user/item features via language modeling, where the collaborative and content information can be effectively learned by the user/item token embeddings. ⢠A mutual-regularization strategy is proposed to constrain the CLLM4Rec to learn information more relevant for recommenda- tions from user/item content. In addition, stochastic reordering is proposed such that the order of item tokens can be ignored by the collaborative LLM without influence on the textual parts. ⢠A recommendation-oriented finetuning strategy is proposed for CLLM4Rec, where an item prediction head with multino- mial likelihood is added on the collaborative LLM that predicts hold-out items based on prompt interaction history, where rec- ommendations for multiple items can be generated efficiently.
# 2 RELATED WORK 2.1 Large Language Model (LLM) Basics | 2311.01343#13 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 13,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "We present CLLM4Rec, the first framework that tightly couples the ID paradigm and LLM paradigm of RS, where encoded knowledge and reasoning ability of LLMs can be fully utilized, while user/item ID token embeddings aligned to the vocab space can well capture intrinsic user interests and item properties. ⢠A novel soft+hard prompting strategy is proposed to pretrain the LLMs on sequences of heterogeneous tokens describing user historical interactions and user/item features via language modeling, where the collaborative and content information can be effectively learned by the user/item token embeddings. ⢠A mutual-regularization strategy is proposed to constrain the CLLM4Rec to learn information more relevant for recommenda- tions from user/item content. In addition, stochastic reordering is proposed such that the order of item tokens can be ignored by the collaborative LLM without influence on the textual parts. ⢠A recommendation-oriented finetuning strategy is proposed for CLLM4Rec, where an item prediction head with multino- mial likelihood is added on the collaborative LLM that predicts hold-out items based on prompt interaction history, where rec- ommendations for multiple items can be generated efficiently.\n# 2 RELATED WORK 2.1 Large Language Model (LLM) Basics",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} | [
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] |
2311.04915 | 13 | # 7. Conclusions
In summary, we developed a CoE reasoning prompt for generating empathetic responses based on psychotherapy models, and we the performance of empathetic compared strategy classification. Our findings revealed that LLMs without reasoning showed a significant preference for the exploration strategy, with interpretation being the least preferred strategy. Although all reasoning prompts generated responses most strongly associated with exploration, they differed from the base prompt by generating interpretation to a certain extent. Intriguingly, only the CBT- CoE generated the highest number of the interpretation strategy. This pattern might reflect CBTâs inherent approach - clarifying cognitive errors to clients. These findings incorporating importance of highlight
context-specific therapeutic interactions with generative AIs.
# 8. Limitations and Suggestions
We acknowledge several limitations that should be considered research and development. First, we did not employ more extensive evaluative criteria for empathy, especially those validated from psychology literature like the Interpersonal Reactivity Index (Davis, 1980; Davis, 1983). Future studies should consider evaluating LLMs using their these established scales communication and reproducibility. | 2311.04915#13 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI. | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | [
{
"id": "2302.13971"
},
{
"id": "2305.10601"
},
{
"id": "2212.14402"
},
{
"id": "2205.11916"
},
{
"id": "2108.07258"
},
{
"id": "2209.08141"
},
{
"id": "2201.11903"
},
{
"id": "2306.08997"
},
{
"id": "2303.13375"
},
{
"id": "1808.10399"
}
] | {
"authors": "Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn",
"chunk_id": 13,
"doc_id": "2311.04915",
"primary_category": "cs.CL",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.04915",
"summary": "We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psychotherapy to induce Large Language Models (LLMs) to\nreason about human emotional states. This method is inspired by various\npsychotherapy approaches including Cognitive Behavioral Therapy (CBT),\nDialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality\nTherapy (RT), each leading to different patterns of interpreting clients'\nmental states. LLMs without reasoning generated predominantly exploratory\nresponses. However, when LLMs used CoE reasoning, we found a more comprehensive\nrange of empathetic responses aligned with the different reasoning patterns of\neach psychotherapy model. The CBT based CoE resulted in the most balanced\ngeneration of empathetic responses. The findings underscore the importance of\nunderstanding the emotional context and how it affects human and AI\ncommunication. Our research contributes to understanding how psychotherapeutic\nmodels can be incorporated into LLMs, facilitating the development of\ncontext-specific, safer, and empathetic AI.",
"text": "# 7. Conclusions\nIn summary, we developed a CoE reasoning prompt for generating empathetic responses based on psychotherapy models, and we the performance of empathetic compared strategy classification. Our findings revealed that LLMs without reasoning showed a significant preference for the exploration strategy, with interpretation being the least preferred strategy. Although all reasoning prompts generated responses most strongly associated with exploration, they differed from the base prompt by generating interpretation to a certain extent. Intriguingly, only the CBT- CoE generated the highest number of the interpretation strategy. This pattern might reflect CBTâs inherent approach - clarifying cognitive errors to clients. These findings incorporating importance of highlight\ncontext-specific therapeutic interactions with generative AIs.\n# 8. Limitations and Suggestions\nWe acknowledge several limitations that should be considered research and development. First, we did not employ more extensive evaluative criteria for empathy, especially those validated from psychology literature like the Interpersonal Reactivity Index (Davis, 1980; Davis, 1983). Future studies should consider evaluating LLMs using their these established scales communication and reproducibility.",
"title": "Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models",
"year": 2023
} | [
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2311.01343 | 14 | Transformers with billions of parameters trained on large corpora, i.e., large language models (LLMs), have demonstrated an unprece- dented understanding of natural language and good logical reason- ing ability based on factual knowledge [9]. Based on the part of transformer utilized for language modeling, existing LLMs can be categorized into three classes: encoder-only LLMs, such as BERT [32], encoder-decoder-based LLMs, such as T5 [12], and decoder- only LLMs, such as GPT [11] and LlaMA [13], etc. We focus on LLMs with decoders due to their superior generative abilities compared with the encoder-only models [33]. The training of LLMs is mainly based on two stages. In the pretraining stage, LLMs are trained on large corpora such as website content, Wikipedia, ArXiv paper, and GitHub codes via language modeling (i.e., next/masked token pre- diction), where knowledge in the corpus can be effectively encoded in the weights of the transformer network facilitated by the stacked self-attention modules. Then, during the finetuning stage, exemplar prompt-output pairs (such as questions and answers) or human feedback on multiple generated answers are provided to the LLMs such that they can conduct logical reasoning and generate answers based on the encoded knowledge from the pretrained stage. | 2311.01343#14 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
{
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 14,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "Transformers with billions of parameters trained on large corpora, i.e., large language models (LLMs), have demonstrated an unprece- dented understanding of natural language and good logical reason- ing ability based on factual knowledge [9]. Based on the part of transformer utilized for language modeling, existing LLMs can be categorized into three classes: encoder-only LLMs, such as BERT [32], encoder-decoder-based LLMs, such as T5 [12], and decoder- only LLMs, such as GPT [11] and LlaMA [13], etc. We focus on LLMs with decoders due to their superior generative abilities compared with the encoder-only models [33]. The training of LLMs is mainly based on two stages. In the pretraining stage, LLMs are trained on large corpora such as website content, Wikipedia, ArXiv paper, and GitHub codes via language modeling (i.e., next/masked token pre- diction), where knowledge in the corpus can be effectively encoded in the weights of the transformer network facilitated by the stacked self-attention modules. Then, during the finetuning stage, exemplar prompt-output pairs (such as questions and answers) or human feedback on multiple generated answers are provided to the LLMs such that they can conduct logical reasoning and generate answers based on the encoded knowledge from the pretrained stage.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} | [
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2311.04915 | 14 | Our evaluation focused solely on the empathic accuracy of the LLMsâ and did not measure user perception. User perception of empathetic expression varies depending on whether interact with humans or artificially intelligent systems (Medeiros et al., 2021). Furthermore, people perceive and react differently to AIsâ empathetic expressions (Urakami et al., 2019). Thus, future works should investigate how users perceive and
respond to the modelsâ empathetic responses to enhance our understanding of the efficacy of LLMsâ empathetic expressions.
For quantitative evaluation, we used a single LLM model (GPT-3.5) and one domain, mental health. Incorporating a diverse text corpus, and motivational interviewing (Miller and Rollnick, 2012), could enable LLMs to produce more personalized communication. This presents an opportunity for future research to encompass a wider array of topics and conversational styles, thereby increasing the reliability of LLMâs performance. Additionally, different LLMs may excel in varied capabilities, leading each in LLM specific tasks (Sivarajkumar et al., 2023). Investigating and assessing the empathetic expressions generated by different LLMs is crucial for a comprehensive evaluation of LLMsâ ability to discern human emotions and craft appropriate, empathetic responses.
# 9. Ethical Considerations | 2311.04915#14 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI. | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | [
{
"id": "2302.13971"
},
{
"id": "2305.10601"
},
{
"id": "2212.14402"
},
{
"id": "2205.11916"
},
{
"id": "2108.07258"
},
{
"id": "2209.08141"
},
{
"id": "2201.11903"
},
{
"id": "2306.08997"
},
{
"id": "2303.13375"
},
{
"id": "1808.10399"
}
] | {
"authors": "Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn",
"chunk_id": 14,
"doc_id": "2311.04915",
"primary_category": "cs.CL",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.04915",
"summary": "We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psychotherapy to induce Large Language Models (LLMs) to\nreason about human emotional states. This method is inspired by various\npsychotherapy approaches including Cognitive Behavioral Therapy (CBT),\nDialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality\nTherapy (RT), each leading to different patterns of interpreting clients'\nmental states. LLMs without reasoning generated predominantly exploratory\nresponses. However, when LLMs used CoE reasoning, we found a more comprehensive\nrange of empathetic responses aligned with the different reasoning patterns of\neach psychotherapy model. The CBT based CoE resulted in the most balanced\ngeneration of empathetic responses. The findings underscore the importance of\nunderstanding the emotional context and how it affects human and AI\ncommunication. Our research contributes to understanding how psychotherapeutic\nmodels can be incorporated into LLMs, facilitating the development of\ncontext-specific, safer, and empathetic AI.",
"text": "Our evaluation focused solely on the empathic accuracy of the LLMsâ and did not measure user perception. User perception of empathetic expression varies depending on whether interact with humans or artificially intelligent systems (Medeiros et al., 2021). Furthermore, people perceive and react differently to AIsâ empathetic expressions (Urakami et al., 2019). Thus, future works should investigate how users perceive and\nrespond to the modelsâ empathetic responses to enhance our understanding of the efficacy of LLMsâ empathetic expressions.\nFor quantitative evaluation, we used a single LLM model (GPT-3.5) and one domain, mental health. Incorporating a diverse text corpus, and motivational interviewing (Miller and Rollnick, 2012), could enable LLMs to produce more personalized communication. This presents an opportunity for future research to encompass a wider array of topics and conversational styles, thereby increasing the reliability of LLMâs performance. Additionally, different LLMs may excel in varied capabilities, leading each in LLM specific tasks (Sivarajkumar et al., 2023). Investigating and assessing the empathetic expressions generated by different LLMs is crucial for a comprehensive evaluation of LLMsâ ability to discern human emotions and craft appropriate, empathetic responses.\n# 9. Ethical Considerations",
"title": "Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models",
"year": 2023
} | [
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] |
2311.01343 | 15 | # 2.2 LLM in Recommender Systems
Recently, LLM-based RS has attracted extensive attention from both academia and industry, which are promising to address the long- standing issues of traditional ID-based RSs, such as shallow textual information understanding, poor generalization, etc. [34, 35]. Hou et al. showed that existing LLMs can be viewed as zero-shot rankers,
Collaborative Large Language Model for Recommender Systems | 2311.01343#15 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
{
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 15,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "# 2.2 LLM in Recommender Systems\nRecently, LLM-based RS has attracted extensive attention from both academia and industry, which are promising to address the long- standing issues of traditional ID-based RSs, such as shallow textual information understanding, poor generalization, etc. [34, 35]. Hou et al. showed that existing LLMs can be viewed as zero-shot rankers,\nCollaborative Large Language Model for Recommender Systems",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} | [
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2311.04915 | 15 | # 9. Ethical Considerations
The expanding use of large language models (LLMs), especially within mental healthcare, calls for thoughtful ethical engagement. As these models advance in generating responses that mirror human counselors, it is imperative we closely examine their impact on users, particularly those navigating mental health challenges.
# References
Ahn, Y., Zhang, Y., Park, Y., & Lee, J. (2020). A chatbot solution to chat app problems: Envisioning a chatbot counseling system for teenage victims of online sexual exploitation. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1â7).
American Psychiatric Association, American Psychiatric Association, (1994). Diagnostic and statistical manual of mental disorders: DSM-IV, volume 4. American Psychiatric Association, Washington, DC.
Anderson, C., & Keltner, D. (2002). The role of empathy in the formation and maintenance of social bonds. Behavioral and Brain Sciences, 25(1), 21â22.
Beck, A. T. (1979). Cognitive therapy and the emotional disorders. Penguin.
Bommarito II, M., & Katz, D. M. (2022). Gpt takes preprint exam. bar the arXiv:2212.14402. | 2311.04915#15 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI. | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | [
{
"id": "2302.13971"
},
{
"id": "2305.10601"
},
{
"id": "2212.14402"
},
{
"id": "2205.11916"
},
{
"id": "2108.07258"
},
{
"id": "2209.08141"
},
{
"id": "2201.11903"
},
{
"id": "2306.08997"
},
{
"id": "2303.13375"
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{
"id": "1808.10399"
}
] | {
"authors": "Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn",
"chunk_id": 15,
"doc_id": "2311.04915",
"primary_category": "cs.CL",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.04915",
"summary": "We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psychotherapy to induce Large Language Models (LLMs) to\nreason about human emotional states. This method is inspired by various\npsychotherapy approaches including Cognitive Behavioral Therapy (CBT),\nDialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality\nTherapy (RT), each leading to different patterns of interpreting clients'\nmental states. LLMs without reasoning generated predominantly exploratory\nresponses. However, when LLMs used CoE reasoning, we found a more comprehensive\nrange of empathetic responses aligned with the different reasoning patterns of\neach psychotherapy model. The CBT based CoE resulted in the most balanced\ngeneration of empathetic responses. The findings underscore the importance of\nunderstanding the emotional context and how it affects human and AI\ncommunication. Our research contributes to understanding how psychotherapeutic\nmodels can be incorporated into LLMs, facilitating the development of\ncontext-specific, safer, and empathetic AI.",
"text": "# 9. Ethical Considerations\nThe expanding use of large language models (LLMs), especially within mental healthcare, calls for thoughtful ethical engagement. As these models advance in generating responses that mirror human counselors, it is imperative we closely examine their impact on users, particularly those navigating mental health challenges.\n# References\nAhn, Y., Zhang, Y., Park, Y., & Lee, J. (2020). A chatbot solution to chat app problems: Envisioning a chatbot counseling system for teenage victims of online sexual exploitation. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1â7).\nAmerican Psychiatric Association, American Psychiatric Association, (1994). Diagnostic and statistical manual of mental disorders: DSM-IV, volume 4. American Psychiatric Association, Washington, DC.\nAnderson, C., & Keltner, D. (2002). The role of empathy in the formation and maintenance of social bonds. Behavioral and Brain Sciences, 25(1), 21â22.\nBeck, A. T. (1979). Cognitive therapy and the emotional disorders. Penguin.\nBommarito II, M., & Katz, D. M. (2022). Gpt takes preprint exam. bar the arXiv:2212.14402.",
"title": "Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models",
"year": 2023
} | [
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2311.01343 | 16 | which can rank the relevance of movies based on user historical in- teractions and movie descriptions. However, since pretrained LLMs are not aligned with the recommendation task, more efforts have been devoted to the finetuning of LLMs to obtain recommendation- oriented models. An exemplar work is P5 [20], which finetunes T5 with token sequences transformed from interactions and user/item features, where items are presented by pseudo-IDs in the form of "item_ð". Afterwards, M6 [19] was proposed that combines text infill- ing and auto-regression in the pretraining stage, where pseudo IDs in P5 are completely avoided and replaced by textual descriptions. Recently, TALLRec [36] was proposed where items are represented by both pseudo-ID and textual descriptions. Pseudo-ID-based item representations can easily introduce spurious correlations between irrelevant items. To address this issue, Hua et al. proposed to intro- duce a small number of new tokens, where tokens used to describe the items are determined by their content and collaborative similar- ity. However, representing items with multiple shared tokens can still introduce bias. In addition, for the above | 2311.01343#16 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
{
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{
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{
"id": "2305.07001"
},
{
"id": "2303.13835"
},
{
"id": "2303.18223"
},
{
"id": "2302.03735"
},
{
"id": "2305.00447"
},
{
"id": "2305.08845"
},
{
"id": "2104.08691"
},
{
"id": "2308.10837"
},
{
"id": "2305.06569"
}
] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 16,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "which can rank the relevance of movies based on user historical in- teractions and movie descriptions. However, since pretrained LLMs are not aligned with the recommendation task, more efforts have been devoted to the finetuning of LLMs to obtain recommendation- oriented models. An exemplar work is P5 [20], which finetunes T5 with token sequences transformed from interactions and user/item features, where items are presented by pseudo-IDs in the form of \"item_ð\". Afterwards, M6 [19] was proposed that combines text infill- ing and auto-regression in the pretraining stage, where pseudo IDs in P5 are completely avoided and replaced by textual descriptions. Recently, TALLRec [36] was proposed where items are represented by both pseudo-ID and textual descriptions. Pseudo-ID-based item representations can easily introduce spurious correlations between irrelevant items. To address this issue, Hua et al. proposed to intro- duce a small number of new tokens, where tokens used to describe the items are determined by their content and collaborative similar- ity. However, representing items with multiple shared tokens can still introduce bias. In addition, for the above",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} | [
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] |
2311.04915 | 16 | Bommarito II, M., & Katz, D. M. (2022). Gpt takes preprint exam. bar the arXiv:2212.14402.
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., ... & Bohg, J. (2021). On the opportunities and risks of foundation preprint arXiv:2108.07258.
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Davis, M. H. (1980). Interpersonal reactivity index. | 2311.04915#16 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI. | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | [
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"authors": "Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn",
"chunk_id": 16,
"doc_id": "2311.04915",
"primary_category": "cs.CL",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.04915",
"summary": "We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psychotherapy to induce Large Language Models (LLMs) to\nreason about human emotional states. This method is inspired by various\npsychotherapy approaches including Cognitive Behavioral Therapy (CBT),\nDialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality\nTherapy (RT), each leading to different patterns of interpreting clients'\nmental states. LLMs without reasoning generated predominantly exploratory\nresponses. However, when LLMs used CoE reasoning, we found a more comprehensive\nrange of empathetic responses aligned with the different reasoning patterns of\neach psychotherapy model. The CBT based CoE resulted in the most balanced\ngeneration of empathetic responses. The findings underscore the importance of\nunderstanding the emotional context and how it affects human and AI\ncommunication. Our research contributes to understanding how psychotherapeutic\nmodels can be incorporated into LLMs, facilitating the development of\ncontext-specific, safer, and empathetic AI.",
"text": "Bommarito II, M., & Katz, D. M. (2022). Gpt takes preprint exam. bar the arXiv:2212.14402.\nBommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., ... & Bohg, J. (2021). On the opportunities and risks of foundation preprint arXiv:2108.07258.\nBrown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., ... & Sastry, G. (2020). Language models are few-shot learners. Advances in neural information processing systems, 33, 1877â1901.\nBuechel, S., Buffone, A., Slaff, B., Ungar, L., & Sedoc, J. (2018). Modeling empathy and distress in reaction to news stories. arXiv preprint arXiv:1808.10399.\nCooper, M., & McLeod, J. (2011). Person- centered therapy: A pluralistic perspective. Experiential Person-Centered Psychotherapies, 10(3), 210â223.\nDavis, M. H. (1980). Interpersonal reactivity index.",
"title": "Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models",
"year": 2023
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2311.01343 | 17 | items are determined by their content and collaborative similar- ity. However, representing items with multiple shared tokens can still introduce bias. In addition, for the above methods, candidate items need to be explicitly provided in the prompt when conducting direct recommendation, where the size of candidate pool is limited. Finally, recommendations are generated via autoregression, which is highly inefficient. In summary, the dichotomy between natural language processing and RS still remains to be well addressed. | 2311.01343#17 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
{
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 17,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "items are determined by their content and collaborative similar- ity. However, representing items with multiple shared tokens can still introduce bias. In addition, for the above methods, candidate items need to be explicitly provided in the prompt when conducting direct recommendation, where the size of candidate pool is limited. Finally, recommendations are generated via autoregression, which is highly inefficient. In summary, the dichotomy between natural language processing and RS still remains to be well addressed.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} | [
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] |
2311.04915 | 17 | Davis, M. H. (1980). Interpersonal reactivity index.
Davis, M. H. (1983). Measuring individual for a differences multidimensional of personality and social psychology, 44(1), 113.
De Vignemont, F., & Singer, T. (2006). The
empathic brain: How, when, and why? Trends in Cognitive Sciences, 10(10), 435â441.
Diehl, J. J., Schmitt, L. M., Villano, M., & Crowell, C. R. (2012). The clinical use of robots for individuals with autism spectrum disorders: A critical review. Research in autism spectrum disorders, 6(1), 249â262.
Eisenberg, N. (2014). Altruistic emotion, cognition, and behavior (PLE: Emotion). Psychology Press.
Ekman, P., & Friesen, W. V. (1971). Constants across cultures in the face and emotion. Journal of personality and social psychology, 17(2), 124.
Hall, J. A., & Schwartz, R. (2019). Empathy present and future. The Journal of social psychology, 159(3), 225â243.
Hofmann, S. G., Sawyer, A. T., & Fang, A. (2010). The empirical status of the "new wave" of cognitive behavioral therapy. Psychiatric Clinics, 33(3), 701â710. | 2311.04915#17 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI. | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | [
{
"id": "2302.13971"
},
{
"id": "2305.10601"
},
{
"id": "2212.14402"
},
{
"id": "2205.11916"
},
{
"id": "2108.07258"
},
{
"id": "2209.08141"
},
{
"id": "2201.11903"
},
{
"id": "2306.08997"
},
{
"id": "2303.13375"
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"id": "1808.10399"
}
] | {
"authors": "Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn",
"chunk_id": 17,
"doc_id": "2311.04915",
"primary_category": "cs.CL",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.04915",
"summary": "We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psychotherapy to induce Large Language Models (LLMs) to\nreason about human emotional states. This method is inspired by various\npsychotherapy approaches including Cognitive Behavioral Therapy (CBT),\nDialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality\nTherapy (RT), each leading to different patterns of interpreting clients'\nmental states. LLMs without reasoning generated predominantly exploratory\nresponses. However, when LLMs used CoE reasoning, we found a more comprehensive\nrange of empathetic responses aligned with the different reasoning patterns of\neach psychotherapy model. The CBT based CoE resulted in the most balanced\ngeneration of empathetic responses. The findings underscore the importance of\nunderstanding the emotional context and how it affects human and AI\ncommunication. Our research contributes to understanding how psychotherapeutic\nmodels can be incorporated into LLMs, facilitating the development of\ncontext-specific, safer, and empathetic AI.",
"text": "Davis, M. H. (1980). Interpersonal reactivity index.\nDavis, M. H. (1983). Measuring individual for a differences multidimensional of personality and social psychology, 44(1), 113.\nDe Vignemont, F., & Singer, T. (2006). The\nempathic brain: How, when, and why? Trends in Cognitive Sciences, 10(10), 435â441.\nDiehl, J. J., Schmitt, L. M., Villano, M., & Crowell, C. R. (2012). The clinical use of robots for individuals with autism spectrum disorders: A critical review. Research in autism spectrum disorders, 6(1), 249â262.\nEisenberg, N. (2014). Altruistic emotion, cognition, and behavior (PLE: Emotion). Psychology Press.\nEkman, P., & Friesen, W. V. (1971). Constants across cultures in the face and emotion. Journal of personality and social psychology, 17(2), 124.\nHall, J. A., & Schwartz, R. (2019). Empathy present and future. The Journal of social psychology, 159(3), 225â243.\nHofmann, S. G., Sawyer, A. T., & Fang, A. (2010). The empirical status of the \"new wave\" of cognitive behavioral therapy. Psychiatric Clinics, 33(3), 701â710.",
"title": "Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models",
"year": 2023
} | [
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] |
2311.04915 | 18 | Kaczkurkin, A. N., & Foa, E. B. (2022). Cognitive-behavioral for anxiety disorders: An update on the empirical evidence. Dialogues in Clinical Neuroscience.
Knutson, D., & Koch, J. M. (2022). Person- centered therapy as applied to work with transgender and gender diverse clients. Journal of Humanistic Psychology, 62(1), 104â122.
Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., & Iwasawa, Y. (2022). Large language models are preprint zero-shot arXiv:2205.11916.
Lazarus, R. S. (1991). Emotion and adaptation. Oxford University Press.
Linehan, M. M. (1987). Dialectical behavioral therapy: A cognitive behavioral approach to parasuicide. Journal of Personality Disorders, 1(4), 328â333.
Medeiros, L., Bosse, T., & Gerritsen, C. (2021). Can a chatbot comfort humans? studying the impact of a supportive chatbot on users' self- perceived IEEE Transactions on Human-Machine Systems, 52(3), 343â353.
Miller, W. R., & Rollnick, S. Motivational change. Guilford Press. | 2311.04915#18 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI. | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | [
{
"id": "2302.13971"
},
{
"id": "2305.10601"
},
{
"id": "2212.14402"
},
{
"id": "2205.11916"
},
{
"id": "2108.07258"
},
{
"id": "2209.08141"
},
{
"id": "2201.11903"
},
{
"id": "2306.08997"
},
{
"id": "2303.13375"
},
{
"id": "1808.10399"
}
] | {
"authors": "Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn",
"chunk_id": 18,
"doc_id": "2311.04915",
"primary_category": "cs.CL",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.04915",
"summary": "We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psychotherapy to induce Large Language Models (LLMs) to\nreason about human emotional states. This method is inspired by various\npsychotherapy approaches including Cognitive Behavioral Therapy (CBT),\nDialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality\nTherapy (RT), each leading to different patterns of interpreting clients'\nmental states. LLMs without reasoning generated predominantly exploratory\nresponses. However, when LLMs used CoE reasoning, we found a more comprehensive\nrange of empathetic responses aligned with the different reasoning patterns of\neach psychotherapy model. The CBT based CoE resulted in the most balanced\ngeneration of empathetic responses. The findings underscore the importance of\nunderstanding the emotional context and how it affects human and AI\ncommunication. Our research contributes to understanding how psychotherapeutic\nmodels can be incorporated into LLMs, facilitating the development of\ncontext-specific, safer, and empathetic AI.",
"text": "Kaczkurkin, A. N., & Foa, E. B. (2022). Cognitive-behavioral for anxiety disorders: An update on the empirical evidence. Dialogues in Clinical Neuroscience.\nKnutson, D., & Koch, J. M. (2022). Person- centered therapy as applied to work with transgender and gender diverse clients. Journal of Humanistic Psychology, 62(1), 104â122.\nKojima, T., Gu, S. S., Reid, M., Matsuo, Y., & Iwasawa, Y. (2022). Large language models are preprint zero-shot arXiv:2205.11916.\nLazarus, R. S. (1991). Emotion and adaptation. Oxford University Press.\nLinehan, M. M. (1987). Dialectical behavioral therapy: A cognitive behavioral approach to parasuicide. Journal of Personality Disorders, 1(4), 328â333.\nMedeiros, L., Bosse, T., & Gerritsen, C. (2021). Can a chatbot comfort humans? studying the impact of a supportive chatbot on users' self- perceived IEEE Transactions on Human-Machine Systems, 52(3), 343â353.\nMiller, W. R., & Rollnick, S. Motivational change. Guilford Press.",
"title": "Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models",
"year": 2023
} | [
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] |
2311.01343 | 19 | In this paper, we focus on recommendations with implicit feedback [37]. Consider a system of ð¼ users and ð½ items. We use a binary rating vector rð â {0, 1}ð½ to denote whether user ð has interacted with the ð½ items. In addition, we use xð¢ ð , xð£ ð to denote the textual features associated with user ð and item ð, such as user biography and item content, etc. xð¢ð£ ð ð denotes the textual features associated with both user ð and item ð, such as user ðâs review for item ð. Hereafter, {ð¢,ð£,ð¢ð£ } {ð¢,ð£,ð¢ð£ } {ð,ð,ð ð },ð is a size ð we take a sequential view of x {ð,ð,ð ð } , where x one-hot vector denoting the ðth token in the textual sequence2. In addition, we have a pretrained large language model (LLM), of which we take a | 2311.01343#19 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 19,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "In this paper, we focus on recommendations with implicit feedback [37]. Consider a system of ð¼ users and ð½ items. We use a binary rating vector rð â {0, 1}ð½ to denote whether user ð has interacted with the ð½ items. In addition, we use xð¢ ð , xð£ ð to denote the textual features associated with user ð and item ð, such as user biography and item content, etc. xð¢ð£ ð ð denotes the textual features associated with both user ð and item ð, such as user ðâs review for item ð. Hereafter, {ð¢,ð£,ð¢ð£ } {ð¢,ð£,ð¢ð£ } {ð,ð,ð ð },ð is a size ð we take a sequential view of x {ð,ð,ð ð } , where x one-hot vector denoting the ðth token in the textual sequence2. In addition, we have a pretrained large language model (LLM), of which we take a",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} | [
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] |
2311.04915 | 19 | Miller, W. R., & Rollnick, S. Motivational change. Guilford Press.
(2003). Self-compassion: An Neff, K. alternative conceptualization of a healthy attitude toward oneself. Self and Identity, 2(2), 85â101.
Nori, H., King, N., McKinney, S. M., Carignan, D., & Horvitz, E. (2023). Capabilities of gpt-4 on medical challenge problems. arXiv preprint arXiv:2303.13375.
Nwosu, A., Boardman, S., Husain, M. M., & Doraiswamy, P. M. (2022). Digital therapeutics for mental health: Is attrition the Achilles heel? Frontiers in Psychiatry, 1598.
Prystawski, B., Thibodeau, P., & Goodman, N. (2022). Psychologically-informed chain-of- thought prompts for metaphor understanding in large language models. arXiv preprint arXiv:2209.08141. | 2311.04915#19 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI. | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | [
{
"id": "2302.13971"
},
{
"id": "2305.10601"
},
{
"id": "2212.14402"
},
{
"id": "2205.11916"
},
{
"id": "2108.07258"
},
{
"id": "2209.08141"
},
{
"id": "2201.11903"
},
{
"id": "2306.08997"
},
{
"id": "2303.13375"
},
{
"id": "1808.10399"
}
] | {
"authors": "Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn",
"chunk_id": 19,
"doc_id": "2311.04915",
"primary_category": "cs.CL",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.04915",
"summary": "We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psychotherapy to induce Large Language Models (LLMs) to\nreason about human emotional states. This method is inspired by various\npsychotherapy approaches including Cognitive Behavioral Therapy (CBT),\nDialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality\nTherapy (RT), each leading to different patterns of interpreting clients'\nmental states. LLMs without reasoning generated predominantly exploratory\nresponses. However, when LLMs used CoE reasoning, we found a more comprehensive\nrange of empathetic responses aligned with the different reasoning patterns of\neach psychotherapy model. The CBT based CoE resulted in the most balanced\ngeneration of empathetic responses. The findings underscore the importance of\nunderstanding the emotional context and how it affects human and AI\ncommunication. Our research contributes to understanding how psychotherapeutic\nmodels can be incorporated into LLMs, facilitating the development of\ncontext-specific, safer, and empathetic AI.",
"text": "Miller, W. R., & Rollnick, S. Motivational change. Guilford Press.\n(2003). Self-compassion: An Neff, K. alternative conceptualization of a healthy attitude toward oneself. Self and Identity, 2(2), 85â101.\nNori, H., King, N., McKinney, S. M., Carignan, D., & Horvitz, E. (2023). Capabilities of gpt-4 on medical challenge problems. arXiv preprint arXiv:2303.13375.\nNwosu, A., Boardman, S., Husain, M. M., & Doraiswamy, P. M. (2022). Digital therapeutics for mental health: Is attrition the Achilles heel? Frontiers in Psychiatry, 1598.\nPrystawski, B., Thibodeau, P., & Goodman, N. (2022). Psychologically-informed chain-of- thought prompts for metaphor understanding in large language models. arXiv preprint arXiv:2209.08141.",
"title": "Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models",
"year": 2023
} | [
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2311.01343 | 20 | one-hot vector denoting the ðth token in the textual sequence2. In addition, we have a pretrained large language model (LLM), of which we take a probabilistic view and denote it as ðððð (xð+1|x1:ð ), (ð¿) 1:ð â Rð Ãð¾â via which transform x1:ð into a latent sequence h (ð¿) ð¿ stacked self-attention modules ððð(x1:ð ) and maps the h to ð the probability space of the next token xð+1. Since the LLM is pretrained on large corpora and finetuned on exemplar prompt- answer pairs, the generation is based on logical reasoning with the context information in x1:ð according to its pretrained knowledge. Our aim is to design a new RS that tightly couples the LLM with the recommendation task by introducing user/item ID tokens (and token embeddings), such that user/item semantics (e.g., user inter- ests in item) can be accurately modeled for effective and efficient recommendation whereas | 2311.01343#20 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
{
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 20,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "one-hot vector denoting the ðth token in the textual sequence2. In addition, we have a pretrained large language model (LLM), of which we take a probabilistic view and denote it as ðððð (xð+1|x1:ð ), (ð¿) 1:ð â Rð Ãð¾â via which transform x1:ð into a latent sequence h (ð¿) ð¿ stacked self-attention modules ððð(x1:ð ) and maps the h to ð the probability space of the next token xð+1. Since the LLM is pretrained on large corpora and finetuned on exemplar prompt- answer pairs, the generation is based on logical reasoning with the context information in x1:ð according to its pretrained knowledge. Our aim is to design a new RS that tightly couples the LLM with the recommendation task by introducing user/item ID tokens (and token embeddings), such that user/item semantics (e.g., user inter- ests in item) can be accurately modeled for effective and efficient recommendation whereas",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} | [
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2311.04915 | 20 | Rashkin, H., Smith, E. M., Li, M., & Boureau, Y-L. (2019). Towards empathetic open-domain conversation models: A new benchmark and dataset. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 5370â5381). Association for Computational Linguistics.
Rasouli, S., Gupta, G., Nilsen, E., & Dautenhahn, K. (2022). Potential applications of social robots in robot-assisted interventions for social anxiety. International Journal of Social Robotics, 14(5), 1â32.
Roller, S., Dinan, E., Goyal, N., Ju, D., Williamson, M., Liu, Y., Xu, J., Ott, M., Smith, E. M., Boureau, Y-L., & Weston, J. (2021). Recipes for building an open-domain chatbot. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume (pp. for Computational 300â325). Association Linguistics.
Scherer, K. R., Banse, R., & Wallbott, H. G. from vocal (2001). Emotion expression correlate across languages and cultures. Journal of Cross-cultural psychology, 32(1), 76â92. | 2311.04915#20 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI. | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | [
{
"id": "2302.13971"
},
{
"id": "2305.10601"
},
{
"id": "2212.14402"
},
{
"id": "2205.11916"
},
{
"id": "2108.07258"
},
{
"id": "2209.08141"
},
{
"id": "2201.11903"
},
{
"id": "2306.08997"
},
{
"id": "2303.13375"
},
{
"id": "1808.10399"
}
] | {
"authors": "Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn",
"chunk_id": 20,
"doc_id": "2311.04915",
"primary_category": "cs.CL",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.04915",
"summary": "We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psychotherapy to induce Large Language Models (LLMs) to\nreason about human emotional states. This method is inspired by various\npsychotherapy approaches including Cognitive Behavioral Therapy (CBT),\nDialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality\nTherapy (RT), each leading to different patterns of interpreting clients'\nmental states. LLMs without reasoning generated predominantly exploratory\nresponses. However, when LLMs used CoE reasoning, we found a more comprehensive\nrange of empathetic responses aligned with the different reasoning patterns of\neach psychotherapy model. The CBT based CoE resulted in the most balanced\ngeneration of empathetic responses. The findings underscore the importance of\nunderstanding the emotional context and how it affects human and AI\ncommunication. Our research contributes to understanding how psychotherapeutic\nmodels can be incorporated into LLMs, facilitating the development of\ncontext-specific, safer, and empathetic AI.",
"text": "Rashkin, H., Smith, E. M., Li, M., & Boureau, Y-L. (2019). Towards empathetic open-domain conversation models: A new benchmark and dataset. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 5370â5381). Association for Computational Linguistics.\nRasouli, S., Gupta, G., Nilsen, E., & Dautenhahn, K. (2022). Potential applications of social robots in robot-assisted interventions for social anxiety. International Journal of Social Robotics, 14(5), 1â32.\nRoller, S., Dinan, E., Goyal, N., Ju, D., Williamson, M., Liu, Y., Xu, J., Ott, M., Smith, E. M., Boureau, Y-L., & Weston, J. (2021). Recipes for building an open-domain chatbot. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume (pp. for Computational 300â325). Association Linguistics.\nScherer, K. R., Banse, R., & Wallbott, H. G. from vocal (2001). Emotion expression correlate across languages and cultures. Journal of Cross-cultural psychology, 32(1), 76â92.",
"title": "Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models",
"year": 2023
} | [
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] |
2311.04915 | 21 | Sharma, A., Miner, A., Atkins, D., & Althoff, T. (2020). A to computational understanding empathy expressed in text-based mental health support. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 5263â5276). Association for Computational
Linguistics.
Sivarajkumar, S., Kelley, M., Samolyk- Mazzanti, A., Visweswaran, S., & Wang, Y. (2023). An empirical evaluation of prompting strategies for large language models in zero- shot clinical natural language processing.
Taori, R., Gulrajani, I., Zhang, T., Dubois, Y., Li, X., Guestrin, C., Liang, P., & Hashimoto, T. B. replicable instruction-following model. Stanford Center for Research on Foundation Models. [Online]. at Available https://crfm.stanford.edu/2023/03/13/alpaca.html | 2311.04915#21 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI. | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | [
{
"id": "2302.13971"
},
{
"id": "2305.10601"
},
{
"id": "2212.14402"
},
{
"id": "2205.11916"
},
{
"id": "2108.07258"
},
{
"id": "2209.08141"
},
{
"id": "2201.11903"
},
{
"id": "2306.08997"
},
{
"id": "2303.13375"
},
{
"id": "1808.10399"
}
] | {
"authors": "Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn",
"chunk_id": 21,
"doc_id": "2311.04915",
"primary_category": "cs.CL",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.04915",
"summary": "We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psychotherapy to induce Large Language Models (LLMs) to\nreason about human emotional states. This method is inspired by various\npsychotherapy approaches including Cognitive Behavioral Therapy (CBT),\nDialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality\nTherapy (RT), each leading to different patterns of interpreting clients'\nmental states. LLMs without reasoning generated predominantly exploratory\nresponses. However, when LLMs used CoE reasoning, we found a more comprehensive\nrange of empathetic responses aligned with the different reasoning patterns of\neach psychotherapy model. The CBT based CoE resulted in the most balanced\ngeneration of empathetic responses. The findings underscore the importance of\nunderstanding the emotional context and how it affects human and AI\ncommunication. Our research contributes to understanding how psychotherapeutic\nmodels can be incorporated into LLMs, facilitating the development of\ncontext-specific, safer, and empathetic AI.",
"text": "Sharma, A., Miner, A., Atkins, D., & Althoff, T. (2020). A to computational understanding empathy expressed in text-based mental health support. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 5263â5276). Association for Computational\nLinguistics.\nSivarajkumar, S., Kelley, M., Samolyk- Mazzanti, A., Visweswaran, S., & Wang, Y. (2023). An empirical evaluation of prompting strategies for large language models in zero- shot clinical natural language processing.\nTaori, R., Gulrajani, I., Zhang, T., Dubois, Y., Li, X., Guestrin, C., Liang, P., & Hashimoto, T. B. replicable instruction-following model. Stanford Center for Research on Foundation Models. [Online]. at Available https://crfm.stanford.edu/2023/03/13/alpaca.html",
"title": "Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models",
"year": 2023
} | [
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] |
2311.01343 | 22 | # 3.2 Extension of User/Item Tokens
3.2.1 Vocab Expansion. To tightly couple the pretrained LLM with the recommendation task, we first expand the vocabulary of
2we use ð¢ and ð£ in the superscript to distinguish user or item-related variables.
Conferenceâ17, July 2017, Washington, DC, USA
Vocab Pred. Head t Shared Pretrained LLM Backbone q Ivem Pred. Head Collab LLM, <user_i> has interacted with <item_j> <item_> <item_l>
Figure 2: The overview of the proposed CLLM4Rec in the mutually-regularized pretraining stage. Mutual regulariza- tion of item_k is omitted for simplicity.
the LLM by adding user/item ID tokens to describe the intrinsic user/item semantic, such that semantic gap between RS and natural language can be well bridged. We use bracket notations "<user_ð>" and "<item_ð>" to denote the newly-introduced token for the ðth user and the ðth item, respectively, which has token ID ð + ð and ð + ð¼ + ð, and will not be broken down into atomic tokens. | 2311.01343#22 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
{
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 22,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "# 3.2 Extension of User/Item Tokens\n3.2.1 Vocab Expansion. To tightly couple the pretrained LLM with the recommendation task, we first expand the vocabulary of\n2we use ð¢ and ð£ in the superscript to distinguish user or item-related variables.\nConferenceâ17, July 2017, Washington, DC, USA\nVocab Pred. Head t Shared Pretrained LLM Backbone q Ivem Pred. Head Collab LLM, <user_i> has interacted with <item_j> <item_> <item_l>\nFigure 2: The overview of the proposed CLLM4Rec in the mutually-regularized pretraining stage. Mutual regulariza- tion of item_k is omitted for simplicity.\nthe LLM by adding user/item ID tokens to describe the intrinsic user/item semantic, such that semantic gap between RS and natural language can be well bridged. We use bracket notations \"<user_ð>\" and \"<item_ð>\" to denote the newly-introduced token for the ðth user and the ðth item, respectively, which has token ID ð + ð and ð + ð¼ + ð, and will not be broken down into atomic tokens.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} | [
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] |
2311.04915 | 22 | Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M-A., Lacroix, T., Roziere, B., Goyal, N., Hambro, E., Azhar, F., et al. (2023). Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971.
Truax, C. B., & Carkhuff, R. (2007). Toward effective and psychotherapy: Training and practice. Transaction Publishers.
Urakami, J., Moore, B. A., Sutthithatip, S., & Park, S. (2019). Users' perception of empathic expressions by an advanced intelligent system. In Proceedings of the 7th International Conference on Human-Agent Interaction (pp. 11â18).
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Chi, E., Le, Q., & Zhou, D. (2022). Chain of thought prompting elicits reasoning in large language preprint arXiv:2201.11903.
Wondra, J. D., & Ellsworth, P. C. (2015). An appraisal theory of empathy and other vicarious emotional experiences. Psychological review, 122(3), 411. | 2311.04915#22 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI. | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | [
{
"id": "2302.13971"
},
{
"id": "2305.10601"
},
{
"id": "2212.14402"
},
{
"id": "2205.11916"
},
{
"id": "2108.07258"
},
{
"id": "2209.08141"
},
{
"id": "2201.11903"
},
{
"id": "2306.08997"
},
{
"id": "2303.13375"
},
{
"id": "1808.10399"
}
] | {
"authors": "Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn",
"chunk_id": 22,
"doc_id": "2311.04915",
"primary_category": "cs.CL",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.04915",
"summary": "We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psychotherapy to induce Large Language Models (LLMs) to\nreason about human emotional states. This method is inspired by various\npsychotherapy approaches including Cognitive Behavioral Therapy (CBT),\nDialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality\nTherapy (RT), each leading to different patterns of interpreting clients'\nmental states. LLMs without reasoning generated predominantly exploratory\nresponses. However, when LLMs used CoE reasoning, we found a more comprehensive\nrange of empathetic responses aligned with the different reasoning patterns of\neach psychotherapy model. The CBT based CoE resulted in the most balanced\ngeneration of empathetic responses. The findings underscore the importance of\nunderstanding the emotional context and how it affects human and AI\ncommunication. Our research contributes to understanding how psychotherapeutic\nmodels can be incorporated into LLMs, facilitating the development of\ncontext-specific, safer, and empathetic AI.",
"text": "Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M-A., Lacroix, T., Roziere, B., Goyal, N., Hambro, E., Azhar, F., et al. (2023). Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971.\nTruax, C. B., & Carkhuff, R. (2007). Toward effective and psychotherapy: Training and practice. Transaction Publishers.\nUrakami, J., Moore, B. A., Sutthithatip, S., & Park, S. (2019). Users' perception of empathic expressions by an advanced intelligent system. In Proceedings of the 7th International Conference on Human-Agent Interaction (pp. 11â18).\nWei, J., Wang, X., Schuurmans, D., Bosma, M., Chi, E., Le, Q., & Zhou, D. (2022). Chain of thought prompting elicits reasoning in large language preprint arXiv:2201.11903.\nWondra, J. D., & Ellsworth, P. C. (2015). An appraisal theory of empathy and other vicarious emotional experiences. Psychological review, 122(3), 411.",
"title": "Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models",
"year": 2023
} | [
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] |
2311.01343 | 23 | 3.2.2 Token Embeddings. For LLMs to understand the tokens, they must be first transformed into dense embeddings. Accordingly, we use zð¡ ð â ð
ð¾ to represent the pretrained embedding of the ðth vocab token. In addition, for the newly-introduced user/item tokens, we introduce two types of embeddings to represent user/item col- laborative and content semantics. Specifically, to align the user/item tokens with the vocab space of the pretrained LLM, we sample the user/item collaborative token embeddings from the same size-ð¾ latent space as follows:
aa? ~ N (0. ay! âIk), (1)
where A; is the prior precision for at 2? Importantly, to align the content semantics with the collaborative semantic for more recommendation-oriented content modeling, we sample the user/item content token embeddings from the following conditional prior: | 2311.01343#23 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
{
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 23,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "3.2.2 Token Embeddings. For LLMs to understand the tokens, they must be first transformed into dense embeddings. Accordingly, we use zð¡ ð â ð
ð¾ to represent the pretrained embedding of the ðth vocab token. In addition, for the newly-introduced user/item tokens, we introduce two types of embeddings to represent user/item col- laborative and content semantics. Specifically, to align the user/item tokens with the vocab space of the pretrained LLM, we sample the user/item collaborative token embeddings from the same size-ð¾ latent space as follows:\naa? ~ N (0. ay! âIk), (1)\nwhere A; is the prior precision for at 2? Importantly, to align the content semantics with the collaborative semantic for more recommendation-oriented content modeling, we sample the user/item content token embeddings from the following conditional prior:",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} | [
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] |
2311.04915 | 23 | Wondra, J. D., & Ellsworth, P. C. (2015). An appraisal theory of empathy and other vicarious emotional experiences. Psychological review, 122(3), 411.
Wubbolding, R. E., Casstevens, W. J., & Fulkerson, M. H. (2017). Using the wdep system of reality therapy to support person- treatment planning. Journal of centered Counseling & Development, 95(4), 472â477.
Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T. L., Cao, Y., & Narasimhan, K. (2023). Tree of thoughts: Deliberate problem solving with large language models. arXiv preprint arXiv:2305.10601.
Zaki, J. (2019). The war for kindness: Building empathy in a fractured world. Crown.
Zhang, S. J., Florin, S., Lee, A. N., Niknafs, E., Marginean, A., Wang, A., Tyser, K., Chin, Z., Hicke, Y., Singh, N., et al. (2023). Exploring the MIT mathematics and EECS curriculum using language models. arXiv preprint large arXiv:2306.08997. | 2311.04915#23 | Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models | We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI. | http://arxiv.org/pdf/2311.04915 | Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn | cs.CL, cs.AI, cs.HC | null | null | cs.CL | 20231102 | 20231214 | [
{
"id": "2302.13971"
},
{
"id": "2305.10601"
},
{
"id": "2212.14402"
},
{
"id": "2205.11916"
},
{
"id": "2108.07258"
},
{
"id": "2209.08141"
},
{
"id": "2201.11903"
},
{
"id": "2306.08997"
},
{
"id": "2303.13375"
},
{
"id": "1808.10399"
}
] | {
"authors": "Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn",
"chunk_id": 23,
"doc_id": "2311.04915",
"primary_category": "cs.CL",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.04915",
"summary": "We present a novel method, the Chain of Empathy (CoE) prompting, that\nutilizes insights from psychotherapy to induce Large Language Models (LLMs) to\nreason about human emotional states. This method is inspired by various\npsychotherapy approaches including Cognitive Behavioral Therapy (CBT),\nDialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality\nTherapy (RT), each leading to different patterns of interpreting clients'\nmental states. LLMs without reasoning generated predominantly exploratory\nresponses. However, when LLMs used CoE reasoning, we found a more comprehensive\nrange of empathetic responses aligned with the different reasoning patterns of\neach psychotherapy model. The CBT based CoE resulted in the most balanced\ngeneration of empathetic responses. The findings underscore the importance of\nunderstanding the emotional context and how it affects human and AI\ncommunication. Our research contributes to understanding how psychotherapeutic\nmodels can be incorporated into LLMs, facilitating the development of\ncontext-specific, safer, and empathetic AI.",
"text": "Wondra, J. D., & Ellsworth, P. C. (2015). An appraisal theory of empathy and other vicarious emotional experiences. Psychological review, 122(3), 411.\nWubbolding, R. E., Casstevens, W. J., & Fulkerson, M. H. (2017). Using the wdep system of reality therapy to support person- treatment planning. Journal of centered Counseling & Development, 95(4), 472â477.\nYao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T. L., Cao, Y., & Narasimhan, K. (2023). Tree of thoughts: Deliberate problem solving with large language models. arXiv preprint arXiv:2305.10601.\nZaki, J. (2019). The war for kindness: Building empathy in a fractured world. Crown.\nZhang, S. J., Florin, S., Lee, A. N., Niknafs, E., Marginean, A., Wang, A., Tyser, K., Chin, Z., Hicke, Y., Singh, N., et al. (2023). Exploring the MIT mathematics and EECS curriculum using language models. arXiv preprint large arXiv:2306.08997.",
"title": "Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models",
"year": 2023
} | [
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] |
2311.01343 | 24 | ð,ð¢ ð â¼ N z ð,ð¢ z ð ð,ð£ ð â¼ N ð,ð£ z ð , ðâ1 ð , ðâ1 ð , z . · Ið¾ · Ið¾ (2)
ð,ð¢ where ðð is the precision for the conditional prior of z . The ð horizontally-stacked matrices of vocab/collaborative/content token embeddings are denoted as Zð¡ , Zð,{ð¢,ð£ } , and Zð,{ð¢,ð£ } , respectively3.
3.2.3 CLLM4Rec Base Model. With user/item tokens and the corresponding token embeddings introduced in the previous sub- sections, we are ready to introduce the CLLM4Rec base model with expanded vocabulary. The CLLM4Rec base model is denoted with (ð¿) {ð,ð },1:ð = Ëððð {ð,ð } (x1:ð ), | 2311.01343#24 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 24,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "ð,ð¢ ð â¼ N z ð,ð¢ z ð ð,ð£ ð â¼ N ð,ð£ z ð , ðâ1 ð , ðâ1 ð , z . · Ið¾ · Ið¾ (2)\nð,ð¢ where ðð is the precision for the conditional prior of z . The ð horizontally-stacked matrices of vocab/collaborative/content token embeddings are denoted as Zð¡ , Zð,{ð¢,ð£ } , and Zð,{ð¢,ð£ } , respectively3.\n3.2.3 CLLM4Rec Base Model. With user/item tokens and the corresponding token embeddings introduced in the previous sub- sections, we are ready to introduce the CLLM4Rec base model with expanded vocabulary. The CLLM4Rec base model is denoted with (ð¿) {ð,ð },1:ð = Ëððð {ð,ð } (x1:ð ),",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} | [
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2311.01343 | 25 | (ð¿) which maps the token sequence x1:ð into the hidden space h {ð,ð },1:ð through ð¿ stacked self-attention module (the superscript (ð¿) will be omitted if no ambiguity exists); here, xð is a size ð + ð¼ + ð½ one-hot
3We use super/subscript ð and ð to distinguish the variables related to the collaborative and content model process, respectively.
Conferenceâ17, July 2017, Washington, DC, USA User!ID:0057 Item ID: 0046 Item Title: Wet n Wild Mega Last Lip Color 908C Sugar Plum Fairy Review: The color is a perfect mix of dark purple, red and pink. The only downside is the drying aspect of the lipstick, which I counteract by using lip balm before putting it on. filling as a the main collaborative effectiveness For interactions P and
# Yaochen Zhuâ,1, Liang Wu2, Qi Guo2, Liangjie Hong2, Jundong Li1 | 2311.01343#25 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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{
"id": "2305.06569"
}
] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 25,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "(ð¿) which maps the token sequence x1:ð into the hidden space h {ð,ð },1:ð through ð¿ stacked self-attention module (the superscript (ð¿) will be omitted if no ambiguity exists); here, xð is a size ð + ð¼ + ð½ one-hot\n3We use super/subscript ð and ð to distinguish the variables related to the collaborative and content model process, respectively.\nConferenceâ17, July 2017, Washington, DC, USA User!ID:0057 Item ID: 0046 Item Title: Wet n Wild Mega Last Lip Color 908C Sugar Plum Fairy Review: The color is a perfect mix of dark purple, red and pink. The only downside is the drying aspect of the lipstick, which I counteract by using lip balm before putting it on. filling as a the main collaborative effectiveness For interactions P and\n# Yaochen Zhuâ,1, Liang Wu2, Qi Guo2, Liangjie Hong2, Jundong Li1",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
} | [
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2311.01343 | 26 | # Yaochen Zhuâ,1, Liang Wu2, Qi Guo2, Liangjie Hong2, Jundong Li1
filling the pretexts in detail. Therefore, we can view the first part as a soft+hard prompt and conduct language modeling only on the main text. This encourages the model to focus exclusively on collaborative and content information, such that the stability and effectiveness of language modeling can be substantially enhanced. ð transformed from the historical interactions of user ð can be broken down into the soft+hard prompt ð,ð x ð
Figure 3: Example review data from Amazon Beauty dataset.
(a) Historical Interactions r;: soft+hard prompt x7? . rm item token seq. x | 2311.01343#26 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 26,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "# Yaochen Zhuâ,1, Liang Wu2, Qi Guo2, Liangjie Hong2, Jundong Li1\nfilling the pretexts in detail. Therefore, we can view the first part as a soft+hard prompt and conduct language modeling only on the main text. This encourages the model to focus exclusively on collaborative and content information, such that the stability and effectiveness of language modeling can be substantially enhanced. ð transformed from the historical interactions of user ð can be broken down into the soft+hard prompt ð,ð x ð\nFigure 3: Example review data from Amazon Beauty dataset.\n(a) Historical Interactions r;: soft+hard prompt x7? . rm item token seq. x",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
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] |
2311.01343 | 27 | Figure 3: Example review data from Amazon Beauty dataset.
(a) Historical Interactions r;: soft+hard prompt x7? . rm item token seq. x
vector denoting the token of either a vocab, a user, or an item. In addition, the subscript in Ëððð {ð,ð } denotes which embedding matrix is used to encode the user/item tokens (where ð stands for matrix Zð,{ð¢,ð£ } and ð stands for matrix Zð,{ð¢,ð£ } ). For the CLLM4Rec base Ëððð {ð,ð } , only the user/item token embeddings are trainable, model whereas the vocab embeddings Zð¡ as well as the other parts of the backbone LLM are fixed to preserve the pretrained knowledge. | 2311.01343#27 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
{
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 27,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "Figure 3: Example review data from Amazon Beauty dataset.\n(a) Historical Interactions r;: soft+hard prompt x7? . rm item token seq. x\nvector denoting the token of either a vocab, a user, or an item. In addition, the subscript in Ëððð {ð,ð } denotes which embedding matrix is used to encode the user/item tokens (where ð stands for matrix Zð,{ð¢,ð£ } and ð stands for matrix Zð,{ð¢,ð£ } ). For the CLLM4Rec base Ëððð {ð,ð } , only the user/item token embeddings are trainable, model whereas the vocab embeddings Zð¡ as well as the other parts of the backbone LLM are fixed to preserve the pretrained knowledge.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
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2311.01343 | 28 | Accordingly, we introduce the collaborative LLM by adding an item prediction head ðð : Rð¾â â P(ð½ ) to the CLLM4Rec base model Ëðððð , which maps the final-layer last-step hidden representation hð,â1 calculated via Ëðððð to the item probability space P(ð½ ) to predict the next item token. The weights of ðð are tied with the item collab- orative token embeddings Zð,ð£ as ðð (hð,â1) = softmax(Zð,𣠷 hð,â1). The generative process of the collaborative LLM can be denoted as:
# 3.3 Mutually-Regularized Pretraining | 2311.01343#28 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 28,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "Accordingly, we introduce the collaborative LLM by adding an item prediction head ðð : Rð¾â â P(ð½ ) to the CLLM4Rec base model Ëðððð , which maps the final-layer last-step hidden representation hð,â1 calculated via Ëðððð to the item probability space P(ð½ ) to predict the next item token. The weights of ðð are tied with the item collab- orative token embeddings Zð,ð£ as ðð (hð,â1) = softmax(Zð,𣠷 hð,â1). The generative process of the collaborative LLM can be denoted as:\n# 3.3 Mutually-Regularized Pretraining",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
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] |
2311.01343 | 29 | # 3.3 Mutually-Regularized Pretraining
With CLLM4Rec base model introduced in the previous section, we discuss the mutually-regularized pretraining strategy for CLLM4Rec to learn the user/item collaborative/content token embeddings based on language modeling on corpora established from user- item interactions and user/item textual features, where the encoded knowledge and logical reasoning ability of the pretrained LLM can be fully utilized. The overall process can be referred to in Fig. 2.
rm of Pp Xi eel hr, (Xitel ie Xt ). (4)
ð,ð where the prompt x serves as a context to generate the next ð item token based on previous item tokens. Since the generation of ð,ð x ð,ð+1 requires attending to previous tokens, when maximizing the likelihood, the collaborative LLM pushes the token embeddings of ð,ð¢ user ð, i.e., z , and the token embeddings of the interacted items, i.e., ð ð,ð£ ð,ð£ ð , · · · , to be close to each other, where user/item collaborative z , z ð semantics in recommendation can be accurately captured. | 2311.01343#29 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 29,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "# 3.3 Mutually-Regularized Pretraining\nWith CLLM4Rec base model introduced in the previous section, we discuss the mutually-regularized pretraining strategy for CLLM4Rec to learn the user/item collaborative/content token embeddings based on language modeling on corpora established from user- item interactions and user/item textual features, where the encoded knowledge and logical reasoning ability of the pretrained LLM can be fully utilized. The overall process can be referred to in Fig. 2.\nrm of Pp Xi eel hr, (Xitel ie Xt ). (4)\nð,ð where the prompt x serves as a context to generate the next ð item token based on previous item tokens. Since the generation of ð,ð x ð,ð+1 requires attending to previous tokens, when maximizing the likelihood, the collaborative LLM pushes the token embeddings of ð,ð¢ user ð, i.e., z , and the token embeddings of the interacted items, i.e., ð ð,ð£ ð,ð£ ð , · · · , to be close to each other, where user/item collaborative z , z ð semantics in recommendation can be accurately captured.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
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2311.01343 | 30 | 3.3.1 Recommendation-Specific Corpora. Generally, we can transform the interactions and user/item content features into doc- uments of user/item/vocab token sequences as follows:
# Raw Corpora Transformed from Recommendation Data
Similarly, for the documents transformed from the user/item ð¢ð£,ð content5, it can also naturally be split into a soft+hard prompt x ð ð and the main text x
(a) Historical Interactions rð : <user_ð> has interacted with <item_ð> <item_ð> ... (b) User/Item Textual Features xð¢ The biography of <user_ð> is: Main biography. The content of <item_ð> is: Main contents. <user_ð> writes the review for <item_ð> : Main reviews.
(b) User/Item Textual Features xij vocab seq. xiâ soft+hard prompt x,â | 2311.01343#30 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 30,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "3.3.1 Recommendation-Specific Corpora. Generally, we can transform the interactions and user/item content features into doc- uments of user/item/vocab token sequences as follows:\n# Raw Corpora Transformed from Recommendation Data\nSimilarly, for the documents transformed from the user/item ð¢ð£,ð content5, it can also naturally be split into a soft+hard prompt x ð ð and the main text x\n(a) Historical Interactions rð : <user_ð> has interacted with <item_ð> <item_ð> ... (b) User/Item Textual Features xð¢ The biography of <user_ð> is: Main biography. The content of <item_ð> is: Main contents. <user_ð> writes the review for <item_ð> : Main reviews.\n(b) User/Item Textual Features xij vocab seq. xiâ soft+hard prompt x,â",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
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2311.01343 | 31 | (b) User/Item Textual Features xij vocab seq. xiâ soft+hard prompt x,â
Accordingly, we introduce the content LLM by adding a vocab prediction head ðð : Rð¾â â P(ð ) to the CLLM4Rec base model Ëðððð , which maps the final-layer last-step hidden representation hð,â1 calculated via Ëðððð (which shares the same pretrained LLM with Ëðððð but uses Zð,{ð¢,ð£ } to decode the user/item token) to the vocab probability space. Similarly, the weights of ðð are tied with the vocab embeddings Zð¡ as ðð (hð,â1) = softmax(Zð¡ · hð,â1). The generative process of the content LLM can be denoted as follows: | 2311.01343#31 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 31,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "(b) User/Item Textual Features xij vocab seq. xiâ soft+hard prompt x,â\nAccordingly, we introduce the content LLM by adding a vocab prediction head ðð : Rð¾â â P(ð ) to the CLLM4Rec base model Ëðððð , which maps the final-layer last-step hidden representation hð,â1 calculated via Ëðððð (which shares the same pretrained LLM with Ëðððð but uses Zð,{ð¢,ð£ } to decode the user/item token) to the vocab probability space. Similarly, the weights of ðð are tied with the vocab embeddings Zð¡ as ðð (hð,â1) = softmax(Zð¡ · hð,â1). The generative process of the content LLM can be denoted as follows:",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
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] |
2311.01343 | 32 | where an example based on the Amazon Beauty dataset can be referred to in Fig. 3. However, directly conducting language model- ing on the raw corpora is clearly infeasible, as each document is composed of heterogeneous vocab, user, and item tokens, where the number of meaningful vocab tokens (e.g., â¼ 50k for GPT, and â¼ 30k for T5) can be diluted by the large number of newly introduced user/item tokens with randomly initialized embeddings.
3.3.2 Soft+Hard Prompting. To address the above challenge, we propose a novel soft+hard prompting strategy to facilitate language modeling on RS-specific corpora with heterogeneous user/item/vocab tokens. The strategy is based on a key observation that documents transformed from both user-item interactions rð and user/item tex- tual features xð¢ ð ð can be broken down into two parts: A heterogeneous part composed of soft (user/item) and hard (vocab) tokens providing context information regarding the gist of the doc- ument, and a main text part with homogeneous item/vocab tokens
cm fe uum jum ~ud,p Xie ~ itn, ( ijk ij, 1:k ⢠) () | 2311.01343#32 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 32,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "where an example based on the Amazon Beauty dataset can be referred to in Fig. 3. However, directly conducting language model- ing on the raw corpora is clearly infeasible, as each document is composed of heterogeneous vocab, user, and item tokens, where the number of meaningful vocab tokens (e.g., â¼ 50k for GPT, and â¼ 30k for T5) can be diluted by the large number of newly introduced user/item tokens with randomly initialized embeddings.\n3.3.2 Soft+Hard Prompting. To address the above challenge, we propose a novel soft+hard prompting strategy to facilitate language modeling on RS-specific corpora with heterogeneous user/item/vocab tokens. The strategy is based on a key observation that documents transformed from both user-item interactions rð and user/item tex- tual features xð¢ ð ð can be broken down into two parts: A heterogeneous part composed of soft (user/item) and hard (vocab) tokens providing context information regarding the gist of the doc- ument, and a main text part with homogeneous item/vocab tokens\ncm fe uum jum ~ud,p Xie ~ itn, ( ijk ij, 1:k ⢠) ()",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
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2311.01343 | 33 | cm fe uum jum ~ud,p Xie ~ itn, ( ijk ij, 1:k ⢠) ()
ð¢ð£,ð ð ð,1:ð ð¢ð£,ð ð ð,ð+1 based on previously as the context.
which generates the next vocab token x ð¢ð£,ð ð¢ð£,ð ð ð,1:ð with prompt x ð ð generated vocab tokens x
4We use the superscripts ð and ð to distinguish the prompt and the main text. 5Hereafter, we take xð¢ð£ an example for discussions, which can be easily generalized to the case of xð¢
Collaborative Large Language Model for Recommender Systems | 2311.01343#33 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 33,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "cm fe uum jum ~ud,p Xie ~ itn, ( ijk ij, 1:k ⢠) ()\nð¢ð£,ð ð ð,1:ð ð¢ð£,ð ð ð,ð+1 based on previously as the context.\nwhich generates the next vocab token x ð¢ð£,ð ð¢ð£,ð ð ð,1:ð with prompt x ð ð generated vocab tokens x\n4We use the superscripts ð and ð to distinguish the prompt and the main text. 5Hereafter, we take xð¢ð£ an example for discussions, which can be easily generalized to the case of xð¢\nCollaborative Large Language Model for Recommender Systems",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
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] |
2311.01343 | 34 | Collaborative Large Language Model for Recommender Systems
When maximizing the likelihood, the content information in xð¢ð£,ð can be encoded in the content token embeddings of user ð and item ð,ð¢ ð, i.e., z , where the pretrained knowledge of the LLM can ð be fully utilized. For example, for the reviews shown in Fig. 3, the pretrained LLM will know that <item_46> is a lipstick with dark purple, red, and pink colors and can have side effects of drying lip, and reasons that <user_57> likes the colors but hates the side effects, which can be alleviated by the lip balm. | 2311.01343#34 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
{
"id": "2302.13971"
},
{
"id": "2206.07682"
},
{
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},
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] | {
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 34,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "Collaborative Large Language Model for Recommender Systems\nWhen maximizing the likelihood, the content information in xð¢ð£,ð can be encoded in the content token embeddings of user ð and item ð,ð¢ ð, i.e., z , where the pretrained knowledge of the LLM can ð be fully utilized. For example, for the reviews shown in Fig. 3, the pretrained LLM will know that <item_46> is a lipstick with dark purple, red, and pink colors and can have side effects of drying lip, and reasons that <user_57> likes the colors but hates the side effects, which can be alleviated by the lip balm.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
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2311.01343 | 35 | Discussion. Generally, since the "hard" (i.e., the vocab) part of ð,ð the prompts x is what the pretrained LLM could un- ð derstand, they are designed to trigger the reasoning ability of the pretrained LLM based on its encoded knowledge. For example, the ð,ð relational phrase "has interacted with" in the prompt x guides ð the collaborative LLM to understand that the newly-introduced ð,ð token <user_i> is a user subject and the tokens in the prompt x ð are the objects of interacted item sequences. Meanwhile, the con- ð¢ð£,ð texts "write the review for" in x direct the content LLM to ð ð , i.e., <user_ð>âs better understand the nature of main texts in x judgment on the <item_ð> based on the personal using experience. The specific formulation of the prompt can be flexible, as Geng et al. has demonstrated that the variation in the expression of the prompt makes less difference, as long as the meaning is the same and the prompt is consistent across the training and testing phases. | 2311.01343#35 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 35,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "Discussion. Generally, since the \"hard\" (i.e., the vocab) part of ð,ð the prompts x is what the pretrained LLM could un- ð derstand, they are designed to trigger the reasoning ability of the pretrained LLM based on its encoded knowledge. For example, the ð,ð relational phrase \"has interacted with\" in the prompt x guides ð the collaborative LLM to understand that the newly-introduced ð,ð token <user_i> is a user subject and the tokens in the prompt x ð are the objects of interacted item sequences. Meanwhile, the con- ð¢ð£,ð texts \"write the review for\" in x direct the content LLM to ð ð , i.e., <user_ð>âs better understand the nature of main texts in x judgment on the <item_ð> based on the personal using experience. The specific formulation of the prompt can be flexible, as Geng et al. has demonstrated that the variation in the expression of the prompt makes less difference, as long as the meaning is the same and the prompt is consistent across the training and testing phases.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
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2311.01343 | 36 | 3.3.3 Mutually-Regularization. Since the pretrained LLMs are not recommendation-oriented, naively optimizing the language modeling objective as Eq. (5) unavoidably captures noise irrele- vant to recommendations. In addition, since the user/item interac- tions are sparse, the collaborative LLM can easily overfit on the ob- served interactions. To address this issue, we propose the mutually- regularized pretraining for CLLM4Rec, where collaborative LLM can guide content LLM to capture recommendation-oriented in- formation from user/item content, and content LLM can in turn introduce side information to support collaborative filtering.
The mutual-regularization naturally arises with the generative process of the CLLM4Rec pretraining stage defined in the previous subsections. If we denote the stacked item token embeddings as ð,ð£ , which contains item ð and other items interacted by the Z ð user ð, the generation process of CLLM4Rec associated with xð ð and xð¢ð£ ð ð can be defined as the joint distribution as follows: | 2311.01343#36 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 36,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "3.3.3 Mutually-Regularization. Since the pretrained LLMs are not recommendation-oriented, naively optimizing the language modeling objective as Eq. (5) unavoidably captures noise irrele- vant to recommendations. In addition, since the user/item interac- tions are sparse, the collaborative LLM can easily overfit on the ob- served interactions. To address this issue, we propose the mutually- regularized pretraining for CLLM4Rec, where collaborative LLM can guide content LLM to capture recommendation-oriented in- formation from user/item content, and content LLM can in turn introduce side information to support collaborative filtering.\nThe mutual-regularization naturally arises with the generative process of the CLLM4Rec pretraining stage defined in the previous subsections. If we denote the stacked item token embeddings as ð,ð£ , which contains item ð and other items interacted by the Z ð user ð, the generation process of CLLM4Rec associated with xð ð and xð¢ð£ ð ð can be defined as the joint distribution as follows:",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
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] |
2311.01343 | 37 | rm .uam Lu glo cu 9c0| 1p uop) _ p(x, Kip 8 Zy 27 2; Ix; Xi) = ti rm orm rp). fe uv,m|_uv,m uvp) TAP him, ck Pike) MP Size Pajakâv Xi LM for collab. LLM ull, 0} 1, Li 1 (2 lai") Te (25 leh?) -» (ai) - Tap (242) LM for content LLM mutual regularization prior
(6) A scrutiny of Eq. (6) reveals that the joint distribution can be decom- posed into three parts: 1) the language modeling of the collaborative and content LLMs that learn user/item token embeddings as Eqs. (4) and (5); 2) the mutual regularization that connects the user/item token embeddings of the two LLMs (i.e., according to Eqs. (1-2),
Conferenceâ17, July 2017, Washington, DC, USA | 2311.01343#37 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 37,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "rm .uam Lu glo cu 9c0| 1p uop) _ p(x, Kip 8 Zy 27 2; Ix; Xi) = ti rm orm rp). fe uv,m|_uv,m uvp) TAP him, ck Pike) MP Size Pajakâv Xi LM for collab. LLM ull, 0} 1, Li 1 (2 lai\") Te (25 leh?) -» (ai) - Tap (242) LM for content LLM mutual regularization prior\n(6) A scrutiny of Eq. (6) reveals that the joint distribution can be decom- posed into three parts: 1) the language modeling of the collaborative and content LLMs that learn user/item token embeddings as Eqs. (4) and (5); 2) the mutual regularization that connects the user/item token embeddings of the two LLMs (i.e., according to Eqs. (1-2),\nConferenceâ17, July 2017, Washington, DC, USA",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
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] |
2311.01343 | 38 | Conferenceâ17, July 2017, Washington, DC, USA
Pp (2",") and p (242¢) are conditional Gaussians, which will introduce MSE regularization between a ght, and z co Lo 12; Lik when ik? i log-likelihood is maximized) 3) the prior of gin and ai » which will be ignored due to the existence of mutual regularization (i.e., setting the precision A; in the prior in Eq. (1) as zero).
We use Maximum a Posteriori (MAP) to estimate the user/item ð,ð£ ð,ð¢ , Z token embeddings z , where the objective is pro- ð ð portional to the logarithm of the joint distribution specified in Eq. (4). We take alternative steps to optimize the MAP objective. If we denote the trainable parameters associated with the item token prediction head ðð and vocab token prediction head ðð as ð½ð (which are tied with the corresponding token embeddings), the objective for the collaborative LLM (L-step) and content LLM (C-step) with mutual regularization can be derived as follows: | 2311.01343#38 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 38,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "Conferenceâ17, July 2017, Washington, DC, USA\nPp (2\"\u0002,\") and p (242¢) are conditional Gaussians, which will introduce MSE regularization between a ght, and z co Lo 12; Lik when ik? i log-likelihood is maximized) 3) the prior of gin and ai » which will be ignored due to the existence of mutual regularization (i.e., setting the precision A; in the prior in Eq. (1) as zero).\nWe use Maximum a Posteriori (MAP) to estimate the user/item ð,ð£ ð,ð¢ , Z token embeddings z , where the objective is pro- ð ð portional to the logarithm of the joint distribution specified in Eq. (4). We take alternative steps to optimize the MAP objective. If we denote the trainable parameters associated with the item token prediction head ðð and vocab token prediction head ðð as ð½ð (which are tied with the corresponding token embeddings), the objective for the collaborative LLM (L-step) and content LLM (C-step) with mutual regularization can be derived as follows:",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
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2311.01343 | 39 | L-step. In the L-step, we fix user/item content embeddings aan Vis as a, Via in Eq. (6), and use them to constrain the user/item collaborative embeddings along with the language modeling of collaborative LLM, leading to the following composite objective: MAP (,Lu jlo _ -> fi rm|orm np LY step (2; »Z; 6) = DP in, xe ke Xi ke
MAP (,Lu jlo _ -> fi rm|orm np LY step (2; »Z; 6) = DP in, xe ke Xi ke LM loss for collab. LLM Ae || Lu _ zeul|? Ac || bo gcoll® â Ar || tull Az | Le Bre -5 $a Fe -2B k MR loss with content LLM Prior loss
# ð,ð£ , Z ð
+ Cð ,
(7) where Cð is the constant irrelevant for optimization. The LM loss captures the collaborative similarity between token embeddings of user ð and the interacted items, where side information can be introduced via the MR loss to support collaborative filtering. | 2311.01343#39 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 39,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "L-step. In the L-step, we fix user/item content embeddings aan Vis as a, Via in Eq. (6), and use them to constrain the user/item collaborative embeddings along with the language modeling of collaborative LLM, leading to the following composite objective: MAP (,Lu jlo _ -> fi rm|orm np LY step (2; »Z; 6) = DP in, xe ke Xi ke\nMAP (,Lu jlo _ -> fi rm|orm np LY step (2; »Z; 6) = DP in, xe ke Xi ke LM loss for collab. LLM Ae || Lu _ zeul|? Ac || bo gcoll® â Ar || tull Az | Le Bre -5 $a Fe -2B k MR loss with content LLM Prior loss\n# ð,ð£ , Z ð\n+ Cð ,\n(7) where Cð is the constant irrelevant for optimization. The LM loss captures the collaborative similarity between token embeddings of user ð and the interacted items, where side information can be introduced via the MR loss to support collaborative filtering.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
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2311.01343 | 40 | C-step. After one-step optimization of the L-step, we fix the user/item ð,ð¢ collaborative token embeddings z in Eq. (6), lead- ð ing to the following composite objective for the content LLM:
MAP [,c,u co a Te uv,m|_uo,m uv,p Le step (2; me] 8) = dep f; (xin PG jickâv % ) k Ime
LM loss for content LLM |Lao _ gholl* 4 0 J 200°
Ae Jou _ ghul? Ae 2% tlle J MR loss with collab. LLM
Ae Jou _ ghul? Ae |Lao _ gholl* 4 0 2% tlle J 200°
(8)
where MR loss constrains content LLM to capture recommendation- oriented information from user/item textual features. In Eqs. (7) and (8), ðð controls the strength of mutual regularization, which will be thoroughly discussed in the empirical study. | 2311.01343#40 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 40,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "C-step. After one-step optimization of the L-step, we fix the user/item ð,ð¢ collaborative token embeddings z in Eq. (6), lead- ð ing to the following composite objective for the content LLM:\nMAP [,c,u co a Te uv,m|_uo,m uv,p Le step (2; me] 8) = dep f; (xin PG jickâv % ) k Ime\nLM loss for content LLM |Lao _ gholl* 4 0 J 200°\nAe Jou _ ghul? Ae 2% tlle J MR loss with collab. LLM\nAe Jou _ ghul? Ae |Lao _ gholl* 4 0 2% tlle J 200°\n(8)\nwhere MR loss constrains content LLM to capture recommendation- oriented information from user/item textual features. In Eqs. (7) and (8), ðð controls the strength of mutual regularization, which will be thoroughly discussed in the empirical study.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
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2311.01343 | 41 | 3.3.4 Stochastic Item Reordering. Another issue that hinders effective collaborative filtering via Eq. (7) is the order of item to- kens when transforming the historical interactions rð into a token ð,ð sequence x for language modeling. Item order usually does not ð matter for collaborative filtering (even if it matters, the positional embeddings denoting the order of natural language may not cap- ture the semantics of the order of interactions). To address this ð,ð issue, we propose to randomly permute the item tokens in x ð
Conferenceâ17, July 2017, Washington, DC, USA
ð,ð with prompt x ð fixed when optimizing the collaborative LLM as Eq. (7). Through this strategy, the order of interacted items can be ð,ð ignored without negative influence on the vocab tokens in x ð
# 3.4 Recommendation-Oriented Finetuning | 2311.01343#41 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 41,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "3.3.4 Stochastic Item Reordering. Another issue that hinders effective collaborative filtering via Eq. (7) is the order of item to- kens when transforming the historical interactions rð into a token ð,ð sequence x for language modeling. Item order usually does not ð matter for collaborative filtering (even if it matters, the positional embeddings denoting the order of natural language may not cap- ture the semantics of the order of interactions). To address this ð,ð issue, we propose to randomly permute the item tokens in x ð\nConferenceâ17, July 2017, Washington, DC, USA\nð,ð with prompt x ð fixed when optimizing the collaborative LLM as Eq. (7). Through this strategy, the order of interacted items can be ð,ð ignored without negative influence on the vocab tokens in x ð\n# 3.4 Recommendation-Oriented Finetuning",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
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] |
2311.01343 | 42 | # 3.4 Recommendation-Oriented Finetuning
3.4.1 Pretraining v.s. Finetuning. The pretraining of CLLM4Rec aims to learn user/item token embeddings based on the large cor- pus of documents transformed from user-item interactions rð and ð , xð¢ð£ user/item textual features xð¢ ð ð via language modeling. How- ever, for now, the pretrained CLLM4Rec can only complete item/vocab token sequences based on the soft+hard prompts, and therefore the gap between NLP and RS is still not completely eliminated. In addition, naively treating the collaborative LLM as a recom- mendation model can lead to huge computational costs where the recommended items are sequentially generated via auto-regression. Therefore, we propose a recommendation-oriented finetuning strat- egy for CLLM4Rec, which aims to finetune the pretrained collabo- rative LLM and tailor it for efficient recommendations. | 2311.01343#42 | Collaborative Large Language Model for Recommender Systems | Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently. | http://arxiv.org/pdf/2311.01343 | Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li | cs.IR | null | null | cs.IR | 20231102 | 20231108 | [
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"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 42,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "# 3.4 Recommendation-Oriented Finetuning\n3.4.1 Pretraining v.s. Finetuning. The pretraining of CLLM4Rec aims to learn user/item token embeddings based on the large cor- pus of documents transformed from user-item interactions rð and ð , xð¢ð£ user/item textual features xð¢ ð ð via language modeling. How- ever, for now, the pretrained CLLM4Rec can only complete item/vocab token sequences based on the soft+hard prompts, and therefore the gap between NLP and RS is still not completely eliminated. In addition, naively treating the collaborative LLM as a recom- mendation model can lead to huge computational costs where the recommended items are sequentially generated via auto-regression. Therefore, we propose a recommendation-oriented finetuning strat- egy for CLLM4Rec, which aims to finetune the pretrained collabo- rative LLM and tailor it for efficient recommendations.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
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