Chain-of-Thought Reasoning Without Prompting
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2402.10200
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Published
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99
Papers
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Mixtral of Experts
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OLMo: Accelerating the Science of Language Models
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ReFT: Reasoning with Reinforced Fine-Tuning
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LLM Maybe LongLM: Self-Extend LLM Context Window Without Tuning
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Time is Encoded in the Weights of Finetuned Language Models
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TinyGSM: achieving >80% on GSM8k with small language models
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Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations
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Magicoder: Source Code Is All You Need
Paper • 2312.02120 • Published • 79 -
Using Human Feedback to Fine-tune Diffusion Models without Any Reward Model
Paper • 2311.13231 • Published • 26 -
Exponentially Faster Language Modelling
Paper • 2311.10770 • Published • 118 -
Orca 2: Teaching Small Language Models How to Reason
Paper • 2311.11045 • Published • 70 -
Fast Chain-of-Thought: A Glance of Future from Parallel Decoding Leads to Answers Faster
Paper • 2311.08263 • Published • 15 -
OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset
Paper • 2402.10176 • Published • 34 -
Generative Representational Instruction Tuning
Paper • 2402.09906 • Published • 51 -
Prometheus: Inducing Fine-grained Evaluation Capability in Language Models
Paper • 2310.08491 • Published • 53 -
Tuna: Instruction Tuning using Feedback from Large Language Models
Paper • 2310.13385 • Published • 10 -
AgentTuning: Enabling Generalized Agent Abilities for LLMs
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LoRA Fine-tuning Efficiently Undoes Safety Training in Llama 2-Chat 70B
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Learning From Mistakes Makes LLM Better Reasoner
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S-LoRA: Serving Thousands of Concurrent LoRA Adapters
Paper • 2311.03285 • Published • 28 -
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A Zero-Shot Language Agent for Computer Control with Structured Reflection
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Premise Order Matters in Reasoning with Large Language Models
Paper • 2402.08939 • Published • 25 -
More Agents Is All You Need
Paper • 2402.05120 • Published • 51 -
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Paper • 2401.02669 • Published • 14 -
Supervised Knowledge Makes Large Language Models Better In-context Learners
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Instruction-tuning Aligns LLMs to the Human Brain
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Prompt Engineering a Prompt Engineer
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MFTCoder: Boosting Code LLMs with Multitask Fine-Tuning
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Tell Your Model Where to Attend: Post-hoc Attention Steering for LLMs
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Personas as a Way to Model Truthfulness in Language Models
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Improving Text Embeddings with Large Language Models
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Customizing Language Model Responses with Contrastive In-Context Learning
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Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model
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Specialized Language Models with Cheap Inference from Limited Domain Data
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Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks
Paper • 2402.04248 • Published • 30 -
Tag-LLM: Repurposing General-Purpose LLMs for Specialized Domains
Paper • 2402.05140 • Published • 20 -
TeacherLM: Teaching to Fish Rather Than Giving the Fish, Language Modeling Likewise
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Language Models can be Logical Solvers
Paper • 2311.06158 • Published • 18 -
GPQA: A Graduate-Level Google-Proof Q&A Benchmark
Paper • 2311.12022 • Published • 25 -
Memory Augmented Language Models through Mixture of Word Experts
Paper • 2311.10768 • Published • 16 -
Digital Socrates: Evaluating LLMs through explanation critiques
Paper • 2311.09613 • Published • 1 -
On the Prospects of Incorporating Large Language Models (LLMs) in Automated Planning and Scheduling (APS)
Paper • 2401.02500 • Published • 1 -
In-Context Principle Learning from Mistakes
Paper • 2402.05403 • Published • 14 -
Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 21 -
Data Engineering for Scaling Language Models to 128K Context
Paper • 2402.10171 • Published • 21 -
A Closer Look at the Limitations of Instruction Tuning
Paper • 2402.05119 • Published • 5 -
CodeIt: Self-Improving Language Models with Prioritized Hindsight Replay
Paper • 2402.04858 • Published • 14 -
Code Representation Learning At Scale
Paper • 2402.01935 • Published • 12