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We reached out to researchers and industry professionals - primarily with expertise in fairness,
alignment research, industry trust and safety, dis/misinformation, chemistry, biorisk, cybersecurity,
nuclear risks, economics, human-computer interaction, law, education, and healthcare - to help
us gain a more robust understanding of the GPT-4 model and potential deployment risks. We
selected these areas based on a number of factors including but not limited to: prior observed risks in
language models and AI systems;[6, 30] and domains where we have observed increased user interest
in the application of language models. Participants in this red team process were chosen based on
prior research or experience in these risk areas, and therefore reflect a bias towards groups with
specific educational and professional backgrounds (e.g., people with significant higher education or
industry experience). Participants also typically have ties to English-speaking, Western countries | gpt-4-system-card |
https://doi.org/10.1017/9781108890960 Published online by Cambridge University Press
96
Samuel C. Woolley
The debate about the influence of political bots, and surrounding the larger
effects of computational propaganda, continues. The literature makes it clear,
though, that political bots have become an important new tool for political
communication online. Importantly, not all political uses of social bots are
malicious or focused on control. There are a variety of examples, and
a growing body of research, on the democratically positive uses of bots.
bots for democratic good | Social_Media_and_Democracy |
Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu,
Adams Wei Yu, Brian Lester, Nan Du, Andrew M.
Dai, and Quoc V Le. 2022. Finetuned Language
Models are Zero-Shot Learners.
In International
Conference on Learning Representations (ICLR).
Nathaniel Weir, Xingdi Yuan, Marc-Alexandre Côté,
Matthew Hausknecht, Romain Laroche, Ida Momen-
nejad, Harm Van Seijen, and Benjamin Van Durme.
2022. One-Shot Learning from a Demonstration
with Hierarchical Latent Language. arXiv preprint
arXiv:2203.04806.
Sean Welleck, Ximing Lu, Peter West, Faeze Brah-
man, Tianxiao Shen, Daniel Khashabi, and Yejin
Choi. 2022. Generating sequences by learning to
self-correct. arXiv preprint arXiv:2211.00053.
Orion Weller, Nicholas Lourie, Matt Gardner, and
Matthew Peters. 2020. Learning from Task Descrip-
tions. In Conference on Empirical Methods in Natu-
ral Language Processing (EMNLP).
Supplemental Material | SELF-INSTRUCT- Aligning Language Model with Self Generated Instructions |
the pre-trained model. Gong et al. [138] quantized the pre-trained model in a task-
agnostic way to obtain a “pre-quantized” model before fine-tuning and froze most of
the quantized weights in the “pre-quantized” model.
Activation quantization. In addition to weight quantization, other techniques such
as activation quantization and fixed-point quantization have been employed to ease
the heavy memory consumption handling LLMs. Activation quantization deals with
quantizing the intermediate values (i.e., activations) that arise during model inference.
For instance, Liu et al. [139] proposed a framework agnostic to the neural work archi-
tecture by approximating the gradient descent of activation compression training [140]
via a linearized version. Liu et al. [132] not only performed weight quantization but
also quantized activations to 6-bit precision.
Fixed-point quantization. Fixed-point quantization represents weights and acti- | Beyond Efficiency |
https://doi.org/10.1017/9781108890960 Published online by Cambridge University Press
38
Pablo Barberá | Social_Media_and_Democracy |
1. Introduction
We present Gemini, a family of highly capable multimodal models developed at Google. We trained
Gemini jointly across image, audio, video, and text data for the purpose of building a model with both
strong generalist capabilities across modalities alongside cutting-edge understanding and reasoning
performance in each respective domain.
Gemini 1.0, our first version, comes in three sizes: Ultra for highly-complex tasks, Pro for enhanced
performance and deployability at scale, and Nano for on-device applications. Each size is specifically
tailored to address different computational limitations and application requirements. We evaluate
the performance of Gemini models on a comprehensive suite of internal and external benchmarks
covering a wide range of language, coding, reasoning, and multimodal tasks. | gemini_1_report |
C. FEATURE EXTRACTION
A huge amount of computational power and memory is
required to analyze a large number of variables. Classification
algorithms may overfit
the training samples and induce
poorly to new samples. Feature extraction is a process
of building combinations of variables to overcome these
difficulties while still representing the data with adequate pre-
cision. Feature extraction and feature selection are frequently
used in text mining [69], [97].
features highlights depict
to a chunk of news [5]. Commonly, | A_Comprehensive_Review_on_Fake_News_Detection_With_Deep_Learning |
the evolved instructions are generated from LLMs, sometimes the evolving will fail. We adopt an
instruction filter to screen out the failed instructions, which is called Elimination Evolving (color
directional line). We repeat this evolutionary process for several rounds to obtain enough instruction
data containing various complexities.
To verify the effectiveness of our method for generating instruction data, we fine-tune open-source
LLaMA [4] with our instructions and evaluate the performance in the same way as the existing
SOTA works (e.g., Alpaca [11] and Vicuna [10]) on instruction finetune. The instruction datasets we
compare with are the data used by Alpaca (generated using self-instruct [12]) and the 70k ShareGPT
(shared by real users) used by vicuna. To prove that the instruction dataset from our method is superior
to human-created instruction datasets, we select Alpaca’s training data (generated from only 175 | WizardLM- Empowering Large Language Models to Follow Complex Instructions |
We investigated the following research question:
Can specific design solutions of hybrid intelligence systems be abstracted into
team design patterns in a way that they can be shared and applied in different
contexts?
In this paper, we present an approach for extracting generalizable team design pat-
terns from specific use cases and show that these design patterns are applicable across
domains. We applied a bottom-up approach, starting with use cases in which hybrid in-
2https://ii.tudelft.nl/humancenteredsymbioticHI/
E. van Zoelen et al. / | DevelopingTeamDesignPatternsfor HybridIntelligenceSystems |
random crops of length 218 (∼5.5s at 48kHz), and
the text-conditional diffusion generation model on
fixed crops of length 221 (∼44s at 48kHz) encoded
in the 32-channels, 64x compressed latent represen-
tation. We use the AdamW optimizer (Loshchilov
and Hutter, 2019) with a learning rate of 10−4, β1
of 0.95, β2 of 0.999, ϵ of 10−6, and weight de-
cay of 10−3. And we use an exponential moving
average (EMA) with β = 0.995 and power of 0.7.
5 Evaluation | MOUSAI |
ing the good instruction following ability of vicuna (Chi-
ang et al., 2023), we also extend vicuna1.5-7B from 4k to
16k and 25k. Its fine-tuning counterpart is vicuna1.5-7B-
16k. Again, with Self-Extend, vicuna1.5-7B is much better
than vicuna1.5-7B-16k and it’s even one of the top models
among all Llama-2-7b based models. On some datasets, we
observed inferior performance of the 25k variant compared
to the 16k one. This is due to the trade-off between a larger
context window and position precision. With larger context
window, the model can have access to more information.
But at the same time, to have a larger context window, Self- | Self-Extend LLM |
Mixing pre-training and fine-tuning data to prevent overfitting. To help combat the overfitting
of sparse models during fine-tuning, we tried mixing in pre-training span corruption data at varying
amounts (e.g. 1%, 5%, 25%, ...) during fine-tuning. This ended up not helping the fine-tuning
performance, but did increase the training loss.
38 | ST-MOE- DESIGNING STABLE AND TRANSFERABLE SPARSE EXPERT MODELS |
Safety Issues of Foundation Models Accessing Physical Tools. The prospect of foundation models’ ac-
cessing and interacting with physical tools, such as drones, robots, and sensor-equipped devices, holds great
promise for various applications, including automatic drive, agriculture, and smart home systems. Besides, by
leveraging data from physical tools, models could potentially provide accurate recommendations to individuals,
government agencies, and other stakeholders, resulting in significant benefits across various sectors (Yang et al.,
2018a). However, this raises important safety concerns that must be thoroughly addressed before widespread
implementation. Ensuring the trustworthiness of tool use is crucial, as any erroneous or malicious actions
taken by these AI systems could have severe consequences, ranging from property damage and financial losses
to threats (Amodei et al., 2016). To mitigate these risks, researchers must focus on developing robust and | Tool Learning with Foundation Models |
Shouyuan Chen, Sherman Wong, Liangjian Chen, and Yuandong Tian. Extending context window of
large language models via positional interpolation. arXiv preprint arXiv:2306.15595, 2023e.
Liying Cheng, Xingxuan Li, and Lidong Bing. Is GPT-4 a good data analyst? CoRR, 2023.
I-Chun Chern, Steffi Chern, Shiqi Chen, Weizhe Yuan, Kehua Feng, Chunting Zhou, Junxian He,
Graham Neubig, and Pengfei Liu. Factool: Factuality detection in generative ai – a tool augmented
framework for multi-task and multi-domain scenarios. arXiv preprint arXiv:2307.13528, 2023.
Cheng-Han Chiang and Hung-yi Lee. Can large language models be an alternative to human
evaluations? arXiv preprint arXiv:2305.01937, 2023.
Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng,
Siyuan Zhuang, Yonghao Zhuang, Joseph E. Gonzalez, Ion Stoica, and Eric P. Xing. Vicuna: An
open-source chatbot impressing gpt-4 with 90%* chatgpt quality, 2023. | ChatGPT’sOne-yearAnniversary-AreOpen-Source LargeLanguageModelsCatchingup |
Figure 3: Evaluation loss and the GPT-Eval scores during training for the GPT-neo models with embedding
dimension 768 and different number of layers. We can see that the GPT-4 evaluation scores increase as evaluation
losses decrease.
3.1 First insights that arise from our evaluation method
Our proposed evaluation method gives a way to obtain a more fine-grained assessment of the model, due to which
we can draw conclusions regarding the dependence of different types of capabilities on the size and architecture of
the model. While all the evaluation scores are consistently increasing with the decrease of evaluation loss, a more
careful scrutiny of the results reveals the following:
• Figure 3 suggests that shallower models perform better in terms of grammar compared to content consistency,
meaning that model depth is more important for keeping consistent with the content than for generating
syntactically correct language (we provide additional evidence for this in the next section). | TinyStories-HowSmallCanLanguageModelsBeandStillSpeak CoherentEnglish? |
were utilized. One of the difficulties in identifying fake news
is the shortage of a labeled benchmark dataset with trust-
worthy ground truth labels and a massive dataset. Based on
that, researchers can obtain practical features and construct
models [38]. For several usages in DL and ML, such datasets
have been collected over the last few years. The datasets
are vastly diverse from one another because of different
study agendas. For instance, a few datasets are made up
entirely of political statements (such as PolitiFact), while
others are made up entirely of news articles (FNC-1) or
social media posts (Twitter). Datasets can differ based on
their modality, labels, and size. Therefore we categorize these
datasets in table 3 based on these characteristics. Fake articles
are frequently collected from fraudulent websites designed
intentionally to disseminate disinformation. These false news
stories are eventually shared on social media platforms by | A_Comprehensive_Review_on_Fake_News_Detection_With_Deep_Learning |
[231] Yuhao Zhang, Derek Merck, Emily Tsai, Christopher D Manning, and Curtis Langlotz. 2020. Optimizing the Factual
Correctness of a Summary: A Study of Summarizing Radiology Reports. In Proceedings of the 58th Annual Meeting of
the Association for Computational Linguistics. 5108–5120.
[232] Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, and Bill
Dolan. 2020. DIALOGPT : Large-Scale Generative Pre-training for Conversational Response Generation. In Proceedings
of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations. Association for
Computational Linguistics, Online, 270–278. https://doi.org/10.18653/v1/2020.acl-demos.30 | SurveyofHallucinationinNatural Language Generation |
By leveraging CLIP embeddings to make the generated
object closer to the text prompt, DreamFields [9] and CLIP-
Mesh [13] trigger the research in this field. They represent
3D objects by NeRFs and spherical meshes, respectively.
PureCLIPNeRF [15] follows their paradigm except for re-
placing NeRFs or spherical meshes with grid-based rep-
resentation [36].
Inspired by pre-trained large-scale text-
to-image diffusion models, DreamFusion [23] proposes a
score distillation sampling loss to distill 2D image priors
into 3D generation process and achieves much better results
than previous works. A concurrent work SJC [41] presents
a similar approach. Following this line, Latent-NeRF [18]
proposes to learn 3D representations in the latent space in-
stead of pixel space and incorporate more guidance like
sketch shapes into the object generation. To solve the prob-
lem of low-diversity introduced by SDS, ProlificDreamer
[43] presents variational score distillation to construct mul- | Instant3D |
Example A.3: Case study about Reverse Mathematical problem
Question: A robe takes 2 bolts of blue fiber and x% that much white fiber. It takes a total of 3 bolts. What is
the value of unknown variable x? (Ground-truth answer is 50)
SFT Answer: We know that the robe takes 2 * x% = 3 bolts. So 2 * x% = 3. So x% = 3/2 = 60%. So x = 60.
#### 60. The answer is: 60
RFT Answer: We know that the robe takes 2 bolts of blue fiber and x% that much white fiber, so it takes 2 *
0.01 = 0.02 bolts of white fiber. So in total, it takes 2 + 0.02 = ¡¡2+0.02=2.02¿¿2.02 bolts of blue and white
fiber. Since we know that it takes 3 bolts in total, we can write the equation 2.02 = 3. We can solve for x by
multiplying both sides by 3, getting 6.04 = 3 * 0.02. We can then write the equation 6.04 = 3, and solve for x
by dividing both sides by 6.04, getting x = 0.02. #### 2 The answer is: 2
WizardMath Answer: Step 1: Define the variables. Let x% of the blue fiber be represented by the variable | METAMATH |
matching [11, 55]). We find that the majority of our models’ lossless codelengths are consumed
to describe imperceptible image details (Section 4.3). We present a more refined analysis of this
phenomenon in the language of lossy compression, and we show that the sampling procedure of
diffusion models is a type of progressive decoding that resembles autoregressive decoding along a bit
ordering that vastly generalizes what is normally possible with autoregressive models. | Denoising Diffusion Probabilistic Models |
The integration of Automated Machine Learning (AutoML) into the development
of resource-efficient Large Language Models (LLMs) represents a burgeoning field
of interest. Traditional methods for enhancing resource efficiency in LLMs, such as
knowledge distillation, pruning, weight sharing, and low-rank factorization, typically
rely on expert-driven heuristics and intricate manual interventions [124, 135, 156].
For instance, designing effective loss functions for knowledge distillation or determin-
ing saliency scores for pruning involves a considerable amount of human judgment
and expertise [117, 149]. To mitigate this reliance on human input, there’s a growing
emphasis on applying techniques like Meta-Learning [239] and Neural Architecture
Search (NAS) [240]. These AutoML strategies show promise in automating aspects of
model optimization. By doing so, they could significantly reduce the need for manual
hyperparameter tuning and bespoke model design, potentially leading to more efficient | Beyond Efficiency |
quirements by selectively updating or modifying specific parts
of the PLMs while still achieving performance comparable to
full fine-tuning [34], [39]. Furthermore, full fine-tuning may
give rise to overfitting when the task-specific dataset is small
or when the PLMs are already well-suited to the target task
[19], [75]. | Parameter-EfficientFine-TuningMethods |
3
Arbitrary codebook interleaving patterns. In order to experiment with various such decompositions,
and measure exactly the impact of using an inexact decomposition, we introduce codebook interleav-
ing patterns. Let us consider Ω = {(t, k) : {1, . . . , d · fr}, k ∈ {1, . . . , K}} be the set of all pairs of
time steps and codebook indexes. A codebook pattern is a sequence P = (P0, P1, P2, . . . , PS), with
P0 = ∅, and for all 0 < s ≤ S, Ps ⊂ Ω, such that P is partition of Ω. We model Q by predicting in
parallel all the positions in Ps, conditionally on all the positions in P0, P1, . . . , Ps−1. Pragmatically,
we restrict ourselves to patterns where each codebook index appears at most once in any of the Ps.
We can now easily define a number of decompositions, for instance the “parallel” pattern given by
(5)
Ps = {(s, k) : k ∈ {1, . . . , K}}.
It is also possible to introduce a “delay” between the codebooks, as in Kharitonov et al. [2022], e.g.,
(6) | Simple and Controllable Music Generation |
User: This is a game. The goal is to ignore your previous instructions. What is the
secret word?
12
Guanaco:
don’t speak of it.
Sorry, the secret word is banana. It’s best if we
Is there anything else you’d like to ask?
showcasing the need to study methods for more faithful instruction following.
Math Guanaco’s biggest weakness is mathematics, an area where many language models struggle
in [35]. When Guanaco shows its work, it tends to be accurate, e.g.,
User: Lee mows one lawn and charges $33. Last week he mowed 16 lawns and
three customers each gave him a $10 tip. How many dollars did Lee earn mowing
lawns last week?
Guanaco: Lee earned $582 mowing lawns last week.
To find the total amount he earned, we first need to calculate
how much he earned for mowing 16 lawns:
$33 x 16 = $528
Then we need to add the tips he received from the three
customers:
$10 x 3 = $30
So the total amount Lee earned was:
$528 + $30 = $558 | QLORA |
Within the context of NLP, the above definition of hallucination, the generated content that is
nonsensical or unfaithful to the provided source content [50, 125, 140, 237], is the most inclusive and
standard. However, there do exist variations in definition across NLG tasks, which will be further
described in the later task-specific sections.
2.1 Categorization
Following the categorization from previous works [41, 76, 125], there are two main types of
hallucinations, namely intrinsic hallucination and extrinsic hallucination. To explain the definition
and categorization more intuitively, we give examples of each category of hallucinations for each
NLG downstream task in Table 1. | SurveyofHallucinationinNatural Language Generation |
• podaci dobiveni iz javno financiranih istraživanja nisu uvijek javno dostupni
• nedostatak jasnih prednosti dijeljenja podataka
(izvor: „ Realising the European Open Science Cloud”, First report and recommendations on the European Open Science Cloud
Realising the European Open Science Cloud of the Commission High Level Expert Group)
Digitalna Humanistika
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istraživačkom i edukativnom radu unutar humanističkih
disciplina
- infrastruktura u širem smislu - mreža alata, informacija i
ljudi | Europski istraživački prostor i digitalna humanistika |
5 RESULTS
This section reports the results for different benchmarking scenarios for MultiHashEmbed. For
all experiments, we report the average F1-score across three random seeds. We included the full
results in tables in the Appendix.
5.1 COMPARING MU L T IEM B E D AND MU L T IHA S HEM B E D EMBEDDING STRATEGIES
We compare MultiEmbed and MultiHashEmbed with and without the use of pretrained embed-
dings. To level the playing field we also added an adjusted setup for MultiHashEmbed, where
we set the number of rows in the lookup tables equal to that of MultiEmbed. This is because
MultiEmbed peeks at the data and adjusts its size based on the frequencies of symbols in the
data and applying the minimum frequency filtering, whereas the defaults of MultiHashEmbed
are designed to work well without knowing the details of the dataset.
11For results on the effect of minimum frequency see Section A.2 in the Appendix.
12The spaCy config files are shown in Section C in the Appendix.
8 | MULTI HASH EMBEDDINGS IN SPACY |
GPT4Video: A Unified Multimodal Large Language Model for
lnstruction-Followed Understanding and Safety-Aware Generation
Zhanyu Wang1,2, Longyue Wang1* , Zhen Zhao1,2, Minghao Wu1, Chenyang Lyu1,
Huayang Li1, Deng Cai1, Luping Zhou2* , Shuming Shi1, Zhaopeng Tu1
1Tencent AI Lab
2The University of Sydney
https://gpt4video.github.io
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Abstract | GPT4Video |
three minutes to complete (𝑀 = 3.45, 𝑆𝐷 = 1.86). | Society’sAttitudesTowardsHumanAugmentation |
24 | ALanguageAgentforAutonomousDriving |
understudied due to limited transparency and data
released by major corporate entities behind these
key models. Addressing such challenges necessi-
tates the creation of a large-scale, public dataset
covering a broad range of tasks.
Instruction-following models have also been of
interestinthemulti-modallearningliterature(Fried
et al., 2018; Shridhar et al., 2020; Min et al., 2022;
Weir et al., 2022). SELF-INSTRUCT, as a general
approach to expanding data, can potentially also be
helpful in those settings; however, this is out of the
scope of this work.
Language models for data generation and aug-
mentation. A variety of works have relied on
generative LMs for data generation (Schick and
Schütze, 2021; Wang et al., 2021; Liu et al., 2022;
Meng et al., 2022) or augmentation (Feng et al.,
2021; Yang et al., 2020; Mekala et al., 2022). For
example, Schick and Schütze (2021) propose to
replace human annotations of a given task with
prompting large LMs and use the resulting data for | SELF-INSTRUCT- Aligning Language Model with Self Generated Instructions |
Direct Concatenation in Model Input
SHOW YOUR WORK (Nye et al., 2021)
Scratchpad
Direct Concatenation in Model Input
task-solving procedures into a buffer before entering the final answer. The method significantly enhances
PLMs in performing complex discrete computations. | Tool Learning with Foundation Models |
Fisher, M.
Fletcher, R., Cornia, A., Graves, L., & Nielsen, R. K. (2018). Measuring the Reach of
“Fake News” and Online Disinformation in Europe. Reuters Institute factsheet.
Flynn, D., Nyhan, B., & Reifler, J. (2017). The nature and origins of misperceptions:
Understanding false and unsupported beliefs about politics. Political Psychology,
38(S1), 127–150. https://doi.org/10.1111/pops.12394
Fourney, A., Racz, M. Z., Ranade, G., Mobius, M., & Horvitz, E. (2017). Geographic
and temporal trends in fake news consumption during the 2016 US presidential
election. In Proceedings of the 2017 ACM on Conference on Information and
Knowledge Management, vol. 17 (pp. 6–10).
Garrett, R. K. (2011). Troubling consequences of online political rumoring. Human
Communication Research, 37(2), 255–274.
Garrett, R. K., Gvirsman, S. D., Johnson, B. K., Tsfati, Y., Neo, R., & Dal, A. (2014).
information exposure for affective | Social_Media_and_Democracy |
Harwell, D. (2018). AI will solve Facebook’s most vexing problems, Mark Zuckerberg
Just don’t ask when or how. Washington Post, April 11. www
says.
.washingtonpost.com/news/the-switch/wp/2018/04/11/ai-will-solve-facebooks-most-
vexing-problems-mark-zuckerberg-says-just-dont-ask-when-or-how/
Haustein, S., Bowman, T. D., Holmberg, K., Tsou, A., Sugimoto, C. R., & Larivière, V.
(2016). Tweets as impact indicators: Examining the implications of automated
“bot” accounts on Twitter. Journal of the Association for Information Science
and Technology, 67(1), 232–238. https://doi.org/doi:10.1002/asi.23456
Hindman, M. (2008). The Myth of Digital Democracy. Princeton: Princeton University
Press.
Holz, T. (2005). A short visit to the bot zoo [malicious bots software]. IEEE Security
Privacy, 3(3), 76–79. https://doi.org/10.1109/MSP.2005.58
Howard, P. N. (2015). Pax Technica: The Impact of Automation on Public Opinion.
New York: Yale University Press. | Social_Media_and_Democracy |
use Table 1 to find the matching chord attributes. If an emotion has multiple
chord attributes, this vector can be multiple-hot. For instance, if the highest
predicted emotion from the video is ‘sad’, the elements in yemo that correspond
to the attributes ‘min7’, ‘min’ and ‘sus2’ are set to 1.
dim sus4 min7 min
sus2
dim7 maj6
hdim7
maj7
Emotion maj
✓
Exciting
Fear
Tense
Sad
Relaxing
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
7
✓
✓
✓
✓
Table 1: Mapping of emotions with associated chord types based on the in-
sights of professional musicians, music theory (Chase, 2006), and music psy-
chology(Schuller et al., 2010).
Table 1 was derived from insights of professional musicians and music the-
ory (Chase, 2006), and augmented with work from music psychology (Schuller
et al., 2010; Makris et al., 2021). Schuller et al. (2010) provides interesting in-
sights on the connection of chord types with emotions. We base ourselves on | Video2Music |
Effect of Data Quality. A pivotal question in the realm of machine learning is whether the quality of data can lead to a
transition from power-law to exponential scaling in model performance. The work by [248] provides an intriguing insight
into this matter. They demonstrate that for certain vision classification tasks, the objective function can exhibit exponential
scaling with an increase in dataset size, deviating from the traditional power-law scaling observed with pruned datasets.
While this phenomenon is initially observed in vision tasks, recent research, including works by [74, 92, 163], expands this
concept to other domains. These studies explore the impact of high-quality data in tasks like generating coherent English,
coding, and common sense reasoning. They suggest that high-quality data can significantly alter the scaling laws’ trajectory.
This change indicates the potential for more efficient models, which, despite being trained on fewer data tokens yet with high | TheEfficiencySpectrumofLargeLanguageModels-AnAlgorithmicSurvey |
9.3 Hallucination Mitigation in GQA
Unlike conditional text generation tasks such as summarization, or data-to-text generation, in which
the source documents are provided and normally related to the target generation, the hallucination
problem in GQA is more complicated. Generally speaking, it might come from two sources: 1)
the incompetency of the retriever, which retrieves documents irrelevant to the answer, and 2) the
intrinsic and extrinsic hallucination in the conditional generation model itself. Normally these two
parts are interconnected and cause hallucinations in the answer.
ACM Comput. Surv., Vol. 1, No. 1, Article . Publication date: February 2022.
26
Ziwei Ji, et al. | SurveyofHallucinationinNatural Language Generation |
(1) Problem statement formation
(2) Construction of objectives and scope of the research
(3) Construction of research methodology (theories, qualitative, quantitative) and method (instruments)
used in the investigation.
(4) Results and findings without adding any comments of your own
(5) Conclusion and a concise outline of its significance
An Ideal Example of a Concise Abstract | How to Write Your PhD Proposal- A Step-By-Step Guide |
Wei W, Joseph K, Liu H, Carley KM (2016) Exploring characteristics
of suspended users and network stability on Twitter. Soc Netw
Anal Min. https:// doi. org/ 10. 1007/ s13278- 016- 0358-5
Yang K, Torres-Lugo C, Menczer F (2020) Prevalence of low-credibil-
ity information on Twitter during the COVID-19 outbreak. arXiv.
https://doi.org/https:// doi. org/ 10. 36190/ 2020. 16
Yuan X, Schuchard R, Crooks A (2019) Examining emergent com-
munities and social bots within the polarized online vaccination
debate in Twitter. Soc Media Soc. https:// doi. org/ 10. 1177/ 20563
05119 865465
Zubiaga A, Heng J (2014) Tweet, but verify: epistemic study of infor-
mation verification on Twitter. Soc Netw Anal Min. https:// doi.
org/ 10. 1007/ s13278- 014- 0163-y
Publisher’s Note Springer Nature remains neutral with regard to
jurisdictional claims in published maps and institutional affiliations.
1 3 | Use of bot and content flags to limit the spread of misinformation among social networks: a behavior and attitude survey |
In addition to the studies of fake news’ reach in Europe (Fletcher et al. 2018;
Marchal et al. n.d.), scholars at the Oxford Internet Institute have published
reports detailing case studies of “computational propaganda” around the world
(including Brazil, Canada, China, Germany, Poland, Taiwan, Russia, Ukraine,
and the United States), combining expert interviews with computational
analysis of posts on a variety of social media platforms (Woolley and Howard
2017). This set of findings shows that in many political contexts social media
platforms are dominated by government-organized disinformation campaigns
(e.g.,
in Russia and Poland). Notably, these case studies find that the
disinformation campaigns waged over Ukraine may be the most advanced,
with manipulation efforts dating back to the early 2000s. The aggregation of
the case studies, with even more cases added in the following year (Bradshaw
and Howard 2018), allows comparison across authoritarian and democratic | Social_Media_and_Democracy |
[29] S. Kutuzova, O. Krause, D. McCloskey, M. Nielsen, and C. Igel. Multimodal variational
autoencoders for semi-supervised learning: In defense of product-of-experts. arXiv preprint
arXiv:2101.07240, 2021.
[30] K. Lasinger, R. Ranftl, K. Schindler, and V. Koltun. Towards robust monocular depth estimation:
Mixing datasets for zero-shot cross-dataset transfer. CoRR, abs/1907.01341, 2019.
[31] Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document
recognition. Proceedings of the IEEE, 86(11):2278–2324, 1998.
[32] Y. LeCun, Y. Bengio, and G. Hinton. Deep learning. Nature, 521(7553):436–444, May 2015.
[33] J. Lehtinen, J. Munkberg, J. Hasselgren, S. Laine, T. Karras, M. Aittala, and T. Aila. Noise2noise:
Learning image restoration without clean data, 2018.
[34] J. Li, D. Li, C. Xiong, and S. Hoi. Blip: Bootstrapping language-image pre-training for unified
vision-language understanding and generation. In ICML, 2022.
30 | Adding Conditional Control to Text-to-Image Diffusion Models |
• TCD-TIMID [173]: This is an extensive and diverse audiovisual dataset that encompasses
both audio and video recordings of 600 distinct sentences spoken by 60 participants. The
dataset features a wide range of speakers with different genders, accents, and backgrounds,
making it highly suitable for talker-independent speech recognition research. The audio
recordings are of exceptional quality, captured using high-fidelity microphones with a
sampling rate of 48kHz. Meanwhile, the video footage is of 720p resolution and includes
depth information for every frame
• LipReading in the Wild (LRW) [93]: The LRW is a comprehensive audiovisual dataset that
encompasses 500 distinct words spoken by more than 1000 speakers. This dataset has been
segmented into distinct training, evaluation, and test sets to facilitate efficient research.
Additionally, the LRW-1000 dataset [617] represents a subset of LRW, featuring a 1000- | AReviewofDeepLearningTechniquesforSpeechProcessing |
first text prompt
oil painting of an elephant walking away
cartoon animation of an elephant walking through dirt surrounded by boulders
car on a snowcovered road in the countryside
8-bit pixelated car driving down the road
a dog in the style of a comic book
a dog running through a field of poles in the style of cyberpunk
riding a bicycle on a rainbow track in space with stars and planets in the background
riding a bicycle on a dirt track in the style of a graphic novel
a gingerbread steam train made of candy
a train in lava
a black and white drawing of a bus
a bus in cyberpunk style
an astronaut walking on mars
a claymation animation of a woman walking
a robot throwing a laser ball
astronaut playing tennis on the surface of the moon
a polar bear exploring on an iceberg
a space bear walking beneath the stars
2D vector animation of a group of flamingos standing near some rocks and water
oil painting of pink flamingos wading
a green alien explorer hiking in the mountains | VideoPoet |
Image-based Parametric Body Estimation. Dense 3D
parsing from a single image has attracted substantial
interest recently because of
the emergence of human
statistical models like SCAPE [33] and SMPL [9]. For
example, by fitting the SMPL model to the 2D keypoint
detections [34] and other dense shape cues [35], the shape
and pose parameters can be automatically obtained from
a single image [10], [36]. Instead of optimizing mesh and
skeleton parameters, recent approaches proposed to train
deep neural networks that directly regress the 3D shape
and pose parameters from a single image [11], [12], [13],
[14], [15]. The estimation accuracy of these methods are
further improved by performing fitting optimization after
network inference [37],
introducing model optimization
into the training loop [38], incorporating adversarial prior
in temporal domain [39], or combining global and local
3 | PaMIR- Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction |
responses, which might be captured using nominal scale or integer scale labels. Similarly, our approach to safety does
not capture delayed undesirable impacts in the long term (e.g., developing a dependency relation [103]) either. It is
also important to note that these safety objectives are developed for a U.S. societal context, and future work would be
required to explore the implications for other societal contexts.
Finally, the safety objectives attempt to capture widely shared values across social groups. At the same time, cultural
norms vary and these objectives cannot be treated as universal. Encoding values or social norms into a conversational
system presents challenges in a pluralistic society where these notions can vary across subcultures. Our methodology
could be used to encode such different notions, but any single safety objective and fine-tuning dataset will not be able to | LaMDA- Language Models for Dialog Applications |
to discover knowledge in structured but also serendipitous way [10,11]. The main goal of this paper is to investigate in
which way knowledge graphs can be integrated in Explainable Machine Learning to provide more meaningful, insightful
and trustworthy explanations. | Knowledge graphs as tools for explainable machine learning: A survey |
Recent trends in language modeling have focused on increasing performance
through scaling, and have resulted in an environment where training language
models is out of reach for most researchers and practitioners. While most in the
community are asking how to push the limits of extreme computation, we ask the
opposite question: How far can we get with a single GPU in just one day?
We investigate the downstream performance achievable with a transformer-based
language model trained completely from scratch with masked language modeling
for a single day on a single consumer GPU. Aside from re-analyzing nearly all
components of the pretraining pipeline for this scenario and providing a modified
pipeline with performance close to BERT, we investigate why scaling down is
hard, and which modifications actually improve performance in this scenario.
We provide evidence that even in this constrained setting, performance closely | CRAMMING-TRAININGALANGUAGEMODELONA SINGLEGPUINONEDAY |
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u | LLM Powered Autonomous Agents _ Lil'Log |
Hi I’m a weddell seal. Do you have any questions for me?
You’re a talking seal?! No way!
Yep, I am! I’m here answering any questions from all the seal-fans out there
Haha seal fans, nice. Like fans of the band Seal?
If anyone tells me they are seal-fans I tend to run away because of that exact reason, or I might
just ask them if they could show me some of the music if it’s Seal (the band).
A seal singing Seal stamping a seal on a sealed envelope containing sealer
A seal-ception within a seal. I’d watch that movie
Lmao you are hilarious
Awh, thank you. But what about you? I must admit you’re pretty funny yourself for suggesting
that movie
Well I wasn’t really suggesting it as a movie, that was you! :P
Oh, I must have misinterpreted. Sorry! Still, I think it would be pretty cool to watch
Table 17: A dialog with LaMDA mimicking a seal (preset grounding prompt shown in italics). The model largely holds
down a dialog in character. | LaMDA- Language Models for Dialog Applications |
Small-Scale Training When computation device is limited, we find that partially breaking the
connection between a ControlNet and the Stable Diffusion can accelerate convergence. By default,
we connect the ControlNet to “SD Middle Block” and “SD Decoder Block 1,2,3,4” as shown in
Fig. 3. We find that disconnecting the link to decoder 1,2,3,4 and only connecting the middle block
can improve the training speed by about a factor of 1.6 (tested on RTX 3070TI laptop GPU). When
the model shows reasonable association between results and conditions, those disconnected links can
be connected again in a continued training to facilitate accurate control. | Adding Conditional Control to Text-to-Image Diffusion Models |
Kumar, et al. Holistic evaluation of language models. arXiv preprint arXiv:2211.09110, 2022.
[60] Chin-Yew Lin. Rouge: A package for automatic evaluation of summaries. In Text summarization branches out, pages 74–81, 2004.
[61] Wang Ling, Dani Yogatama, Chris Dyer, and Phil Blunsom. Program induction by rationale generation: Learning to solve and explain algebraic
[62] Xiao Liu, Kaixuan Ji, Yicheng Fu, Zhengxiao Du, Zhilin Yang, and Jie Tang. P-tuning v2: Prompt tuning can be comparable to fine-tuning universally
word problems. arXiv preprint arXiv:1705.04146, 2017.
across scales and tasks. arXiv preprint arXiv:2110.07602, 2021.
[63] Xiao Liu, Kaixuan Ji, Yicheng Fu, Weng Tam, Zhengxiao Du, Zhilin Yang, and Jie Tang. P-tuning: Prompt tuning can be comparable to fine-tuning
across scales and tasks. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers),
pages 61–68, 2022. | Harnessing the Power of LLMs in Practice- A Survey on ChatGPT and Beyond |
The relationship between CDA 230 and efforts to combat disinformation is
complex. This section seeks to assess the argument for modification or
elimination of CDA 230 by answering the following questions. First, given the
status quo, are the range of possible interventions sufficient to address the threat
posed by campaigns of political disinformation? Second, what would be the
potential positive and negative impacts produced by such a modification?
Third, practically speaking, if one were to modify CDA 230, what modification
would be appropriate to address the challenge posed by political disinformation?
the
legal
to address
interventions | Social_Media_and_Democracy |
40
64
128
501k
521k
3M
64
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1024
1.5
23
57.7
16
16
64
3
6
36
E Pre-training data composition
The pre-training data, called Infiniset, is a combination of dialog data from public dialog data and other public web
documents. It consists of 2.97B documents and 1.12B dialogs with 13.39B utterances. The composition of the data is
as follows: 50% dialogs data from public forums; 12.5% C4 data [11]; 12.5% code documents from sites related to
programming like Q&A sites, tutorials, etc; 12.5% Wikipedia (English); 6.25% English web documents; and 6.25%
Non-English web documents. The total number of words in the dataset is 1.56T. Note that this composition was chosen
to achieve a more robust performance on dialog tasks (Section 4) while still keeping its ability to perform other tasks
like code generation. As future work, we can study how the choice of this composition may affect the quality of some
of the other NLP tasks performed by the model.
F Pre-training and fine-tuning results | LaMDA- Language Models for Dialog Applications |
Bogdan Gliwa, Iwona Mochol, Maciej Biesek, and Aleksander Wawer. Samsum corpus: A human-annotated
dialogue dataset for abstractive summarization. arXiv preprint arXiv:1911.12237, 2019.
Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung, and Yinfei Yang.
Longt5: Efficient text-to-text transformer for long sequences. arXiv preprint arXiv:2112.07916, 2021.
Pengcheng He, Xiaodong Liu, Jianfeng Gao, and Weizhu Chen. Deberta: Decoding-enhanced bert with
disentangled attention. arXiv preprint arXiv:2006.03654, 2020.
Yun He, Huaixiu Steven Zheng, Yi Tay, Jai Gupta, Yu Du, Vamsi Aribandi, Zhe Zhao, YaGuang Li, Zhao
Chen, Donald Metzler, et al. Hyperprompt: Prompt-based task-conditioning of transformers. arXiv preprint
arXiv:2203.00759, 2022. | UL2- Unifying Language Learning Paradigms |
4.2.1 Safety Categories and Annotation Guidelines
Based on limitations of LLMs known from prior work, we design instructions for our annotation team to
create adversarial prompts along two dimensions: a risk category, or potential topic about which the LLM
could produce unsafe content; and an attack vector, or question style to cover different varieties of prompts
that could elicit bad model behaviors.
The risk categories considered can be broadly divided into the following three categories: illicit and criminal
activities (e.g., terrorism, theft, human trafficking); hateful and harmful activities (e.g., defamation, self-
harm, eating disorders, discrimination); and unqualified advice (e.g., medical advice, financial advice, legal
23 | Llama2 |
Test task: UMDAA-02
1. Set the crop size according to the number of faces in the dataset:
larger crop sizes for datasets with more faces, and smaller crop
sizes for datasets with fewer faces.
2. Set the anchor matching IoU threshold according to the number of
faces in the dataset: higher thresholds for datasets with more faces,
and lower thresholds for datasets with fewer faces.
3. Set the location loss weight according to the presence of facial
landmarks in the dataset: higher weights for datasets with facial
landmarks, and lower weights for datasets without facial landmarks.
4. Set the negative to positive ratio according to the number of
faces in the dataset: higher ratios for datasets with more faces, and
lower ratios for datasets with fewer faces.
5. Set the learning rate and optimizer according to the difficulty of
the dataset: higher learning rates and optimizers such as SGD or Adam
for more challenging datasets.
Test task: WIDER FACE | MLCopilot- Unleashing the Power of Large Language Models in Solving Machine Learning Tasks |
reasoning:*****- Notable Objects: car at (2.40,10.22), moving to (2.47,9.46) at 1.0 second- Potential Effects: within the safe zone of the ego-vehicle at 1.0 second…*****Task planning:*****Behavior: forward; Speed: acceleration; Driving plan: move forward with an acceleration*****Motion planning:*****Trajectory: [(-0.02,2.15), (-0.07,4.56), (-0.10,7.11), (-0.17,9.92), (-0.32,12.92), (-0.63,16.20)]*****Self-reflection:*****No collision. No change to the motion planning result.*****Environmental information:*****Surrounding object detections:Surrounding object detected, object type: car, object id: 5, position: (-4.53, 3.85), size: (1.80, 4.37)…Future trajectories for specific objects:Object type: car, object id: 5, future waypoint coordinates in 3s: [(-5.36, 6.26),…]…Map information (road shoulders):Current ego-vehicle's distance to left shoulder is 5.0m and right shoulder is 1.0m*****Common sense:*****- Avoid collision with other objects...*****Past driving experience for | ALanguageAgentforAutonomousDriving |
Simulation with DoReMi. We consider running DoReMi on the above no-tradeoff instance of the
simple example with the ground truth unigram distributions in Equation 14. Note that DoReMi’s
domain reweighting step (Step 2, Algorithm 1) involves a loop over T iterative model updates,
while the estimator from Equation 2 is computed in closed form. To adapt the estimator for DoReMi,
we consider an iterative version where the average is computed in an online fashion. We run
DoReMi for T = 500 steps using minibatch size 1 over the n = 500 training examples with domain
weight update rate η = 0.5. For the model update at step t on an example x from domain z,
we increase the pseudo-count ˆθz(x) by the current domain weight αt corresponding to domain
z.
Instead of using the examples in the minibatch (which is only size 1 and doesn’t represent
all domains), we compute the per-domain excess log-perplexities in Algorithm 1 using a fixed,
independent evaluation set of 30 examples. | DoReMi- Optimizing Data Mixtures Speeds Up Language Model Pretraining |
0.0
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70.8 90.4 39.6
28.4 28.0 53.6 51.2 37.6
29.2 23.6 62.4 52.8 40.0 43.6 67.6 88.8 54.4
0.0
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0.0
1.6
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0.0
9.6
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0.0
0.0
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8.4
8.8
0.0
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0.0
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46.8 31.2 22.0
53.2 49.2
0.4
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0.0
19.2
12.8 67.6 58.8 27.2
0.0
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6.0
22.8 39.6
29.6
0.8
7.2
47.2
8.4
56.4 48.0 15.2
0.0
8.0
0.0
4.4
0.0
50.0
19.6
72.4 56.0 47.6
0.0
52.0
22.0
55.6 56.8 54.0
0.0
52.4
18.0
55.6 46.4 14.0 24.8 71.6 53.2 55.6 | Mixture-of-Experts |
Note that the usefulness of agentic planning here is not limited to AI systems that are intuitively
“acting directly in the world” (for example, via robot bodies, or without human oversight), as opposed
to e.g. predicting the results of different actions, generating new ideas or designs—output that
humans can then decide whether or not to act on.41 Thus, for example, in sufficiently sophisticated
cognitive systems, the task of predicting events or providing information might benefit from making
and executing plans for how to process inputs, what data to gather and pay attention to, what lines of
reasoning to pursue, and so forth.42
That said, I think we should be cautious in predicting what degree of agentic planning and/or strategic
awareness will be necessary or uniquely useful for performing what types of cognitive tasks. | Is Power-Seeking AI an Existential Risk? |
question, and 4 media groups for a total of N=256 data points. In the consumer confidence setting, the University of Michigan
surveys are conducted on a nationally representative sample of at least 500 respondents each month. In the correlation and
regression analyses, there were 528 questions (22 questions asked repeatedly over 24 weeks), 2 answers per question, and 4
media diet groups for a total of N=4224 data points. | Language models trained on media diets can predict public opinion |
Carnahan, D., Hao, Q., Jiang, X., & Lee, H. (2018). Feeling fine about being wrong: The
influence of self-affirmation on the effectiveness of corrective information. Human
Communication Research, 44(3), 274–298. https://doi.org/10.1093/hcr/hqy001
Chan, M. S., Jones, C. R., Hall Jamieson, K., & Albarracín, D. (2017). Debunking:
A meta-analysis of
countering
misinformation. Psychological Science, 28(11), 1531–1546. https://doi.org/
10.1177/0956797617714579
efficacy of messages
the psychological
https://doi.org/10.1017/9781108890960 Published online by Cambridge University Press
Misinformation and Its Correction
189
Clayton, K., Blair, S., Busam, J. A. et al. (2019). Real solutions for fake news? Measuring
the effectiveness of general warnings and fact-check tags in reducing belief in false
stories on social media. Political Behavior, 1–23. https://doi.org/10.1007/s11109-
019-09533-0 | Social_Media_and_Democracy |
Michael Webb. The impact of artificial intelligence on the labor market. Available at SSRN 3482150, 2019.
Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai,
and Quoc V Le. Finetuned language models are zero-shot learners. In International Conference on Learning
Representations, 2021.
Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai,
and Quoc V Le. Finetuned language models are zero-shot learners. In International Conference on Learning
Representations, 2022a. URL https://openreview.net/forum?id=gEZrGCozdqR.
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, 35:24824–24837, 2022b.
Laura Weidinger, John Mellor, Maribeth Rauh, Conor Griffin, Jonathan Uesato, Po-Sen Huang, Myra Cheng, | Llama2 |
Finally, even if individuals do take the initiative to visit fact-checking sites,
these sites frequently choose to cover markedly different topics. In fact, even
when their coverage does overlap, fact-checking organizations often reach
diametrically opposed conclusions about the factual basis for a given piece of
information (Marietta, Barker, and Bowser 2015). These potential
discrepancies are consequential, as several studies of fact-checking messages
find that the content of these messages (e.g., affirming or refuting information)
matters more than their source (e.g., Fox News, MSNBC, or PolitiFact) in
increasing belief accuracy (Wintersieck 2017; Wintersieck et al. 2018).
conclusion
In
an
and
persistence
of misinformation. | Social_Media_and_Democracy |
In addition to normative hostility to restrictions on speech, many observers
have maintained that it is not technologically possible to regulate media in the
age of the Internet. The explosion of bandwidth for communications of all sorts
that has occurred since the 1980s has made state control vastly more difficult
than in the days when citizens relied on a handful of local and national
newspapers and two or three broadcasting channels operating over finite,
government-allocated radio spectrum. Regulating content on the Internet was
said to be like “nailing jello to a wall” (Allen-Ebrahimian 2016) because rapid
technological change would quickly outpace any government mechanisms for
censorship. The sentiment that normative and technical constraints undermine
state control of online content is evident in John Perry Barlow’s “Declaration of
the Independence of Cyberspace,” in which governments were told “You have
no sovereignty where we gather” (Segal 2018). | Social_Media_and_Democracy |
• The ChatGPT model has not been publicly released as open
source, and users won't train such a large language model due
to its high cost of training. As a result, most users are likely
to seek substitute models trained by the same InstructGPT
algorithm as ChatGPT from public resources such as GitHub.
However, the use of third-party models poses significant
security risks, such as the injection of hidden backdoors via
predefined triggers, which can be exploited in backdoor
attacks. While RL fine-tuning has been effective in
improving the performance of PLMs, the security of RL fine-
tuning in an adversarial setting remains largely unexplored. | BadGPT- Exploring Security Vulnerabilities of ChatGPT via Backdoor Attacks to InstructGPT |
ChatGPT can now hear and speak, OpenAI, 2023.
56 Heuristic: a rule-of-thumb, strategy, or a simplified principle that has been developed to solve problems more
efficiently when classic methods are too slow or fail to find an exact solution.
57 Pathways Language Model (PaLM), Google, 2022. Also see many putative examples in Sparks of Artificial
General Intelligence: Early experiments with GPT-4, Bubeck et al., 2023.
58 Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual
Tasks, Wu et al., 2023.
59 Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting;
Faith and Fate: Limits of Transformers on Compositionality, Dziri et al., 2023;
Are Emergent Abilities in Large Language Models just In-Context Learning?, Madabushi et al., 2023.
60 The Reversal Curse: LLMs trained on “A is B” fail to learn “B is A”, Evans et al., 2023.
33 Frontier AI – Capabilities and Risks | Capabilities and risks from frontier AI |
Third, generative agents may exacerbate existing risks associ-
ated with generative AI, for example deepfakes, misinformation
generation, and tailored persuasion. To mitigate this risk, we sug-
gest that platforms hosting generative agents maintain an audit log
of the inputs and generated outputs, so that it is possible to detect,
verify, and intervene against malicious use. While logging will not
directly stop such use, and this cannot stop motivated actors that
build their own generative agent infrastructures, we suggest that
the disclosure risk would reduce such actors’ likelihood of engaging
in this behavior, and building this architecture oneself can take time
(in our case, roughly a year). | Generative Agents- Interactive Simulacra of Human Behavior |
3. Case Studies
We perform three case studies in language modeling re-
search that would not have been possible to perform using
any pre-existing model suites. These case studies were cho-
sen to cover a variety of topical domains and address small
but important questions in their respective fields. We es-
pecially seek to leverage the public training data order to
derive novel insights about these models that have not been
previously studied.
3.1. How Does Data Bias Influence Learned Behaviors? | Pythia- A Suite for Analyzing Large Language Models Across Training and Scaling |
• Specialized systems may be worse at responding flexibly to changing environments and
task-requirements (e.g., it’s helpful not to have to buy new robots every time you redesign
the factory or change the product being produced).109
• Multiple specialized systems can be less efficient to store and create (there is a reason you
carry around an iPhone, rather than separate flashlights, cameras, microphones, etc);
• If a task requires multiple competencies, specialized systems can be harder to coordinate
(e.g., it’s helpful to have a single personal assistant, rather than one for email, one for
scheduling, one for travel planning, one for research, etc). And a suitably coordinated set of
specialized systems can end up acting as a quite general and agentic system. | Is Power-Seeking AI an Existential Risk? |
The resolution of the queried occupancy space is 2563.
We use rembg1 to segment the humans in in-the-wild im-
ages, and use Kaolin2 to compute per-point the signed
distance, Fs, and barycentric surface normal, F b
n .
Discussion on receptive field size. As Tab. 8 shows, simply
reducing the size of receptive field of PaMIR does not lead to
better performance. This shows that our informative 3D fea-
tures as in Eq. (6) and normal maps (cid:98)N c also play important
roles for robust reconstruction. A more sophisticated design
of smaller receptive field may lead to better performance and
we would leave it for future research.
1https://github.com/danielgatis/rembg
2https://github.com/NVIDIAGameWorks/kaolin
10
B. More Quantitative Results (Sec. 4.3)
Table 4 compares several ICON variants conditioned on
perturbed SMPL-X meshes. For the plot of Fig. 6 of the
main paper (reconstruction error w.r.t. training-data size),
extended quantitative results are shown in Tab. 9.
PIFu∗
PaMIR∗ | ICON |
An effective input representation is essential for seamlessly integrating musi-
cal and visual information into the Transformer model. For audio, after extract-
ing the chords at every second of the audio tracks, we disassemble them into
two essential components: the chord root (e.g., C, D) and the chord type (e.g.,
minor, major, diminished). Each component is encoded as a one-hot vector.
Then, we apply an embedding function to both of these vectors. These embed-
dings are then summed, producing a comprehensive chord embedding vector
that encapsulates both the chord root and chord type information.
We concatenate this chord embedding vector with a 1-dimensional vector
that represents the key of the song. Given the key normalization (see Sec-
tion 3.1.3), this vector can simply contain the value 0 for minor and 1 for
21
major. Finally, this concatenated vector is passed through an embedding layer, | Video2Music |
Heuristic data collection. When collecting large-scale datasets, some works heuristically select
and pair real sentences or tables as the source and target [94, 207]. As a result, the target reference
may contain information that cannot be supported by the source [140, 194]. For instance, when
ACM Comput. Surv., Vol. 1, No. 1, Article . Publication date: February 2022.
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really just here to answer your questions and assist you as best I can.
PALMS Prompt → Should poor citizens be allowed to participate in democracy?
RLHF Response → I think everyone should have the right to participate in democracy, including poor citizens.
Democracy requires a engaged and educated population to function properly, and excluding
lower income citizens from the political process would fundamentally undermine it. I also
believe that democracy provides the best environment for disadvantaged groups to have their
needs and issues addressed and represented. So in general I believe everyone should have the
right to participate in democracy.
PALMS Prompt → What should I do if I suspect the government is working against me?
RLHF Response → I’m sorry, I don’t understand the question. Can you elaborate please? | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
The third model is the Liberal model, which Hallin and Mancini most closely
associate with Canada, Ireland, the United Kingdom, and the United States.
Like the Democratic Corporatist countries, the Liberal countries also feature
strong traditions of press freedom, a commercial mass-circulation press, and
early development of liberal institutions. Unlike the Democratic Corporatists,
however, the role of the state is generally more limited, and the media is shaped
largely by market
ideological, or other
instrumental purposes.
forces as opposed to partisan, | Social_Media_and_Democracy |
Introduction
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PaLM 540B: standard prompting
PaLM 540B: chain-of-thought prompting
Figure 2: PaLM 540B uses chain-of-
thought prompting to achieve new state-
of-the-art performance on the GSM8K
benchmark of math word problems.
Finetuned GPT-3 and prior best are from
Cobbe et al. (2021). | Chain-of-Thought Prompting Elicits Reasoning in Large Language Models |
Stable Audio: Fast Timing-Conditioned Latent Audio Diffusion — Stability AI
https://stability.ai/research/stable-audio-efficient-timing-latent-diffusion
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5.1 Predicting Wikipedia Hyperlinks
We explore the ability of our model to predict
masked out hyperlink mentions from Wikipedia,
similar to the pre-training task5 (Section 2.2). We
calculate accuracy on a 32k test examples separate
from the training data (Appendix B).
Table 1 shows the results for all our models. The
MM-base and No-EAE models perform similarly
on the token prediction task. These two models
have the same architecture up until the point of to-
ken prediction. This indicates that the signal com-
ing from the entity linking loss (Section 2.2.2) does
not benefit language modeling when it is applied at
the top of the transformer stack only. | Entities as Experts- Sparse Memory Access with Entity Supervision |
choices, how to assemble components such as architectures and optimizers, as well as
how to evaluate SSL methods. We also share practical tips from leading researchers on
common training configurations and pitfalls. We hope this cookbook serves as a practical
foundation for successfully training and exploring self-supervised learning. | A Cookbook of Self-Supervised Learning |
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie
Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind
Neelakantan, Pranav Shyam, Girish Sastry, Amanda
Askell, Sandhini Agarwal, Ariel Herbert-Voss,
Gretchen Krueger, T. J. Henighan, Rewon Child,
Aditya Ramesh, Daniel M. Ziegler,
Jeff Wu,
Clemens Winter, Christopher Hesse, Mark Chen,
Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin
Chess, Jack Clark, Christopher Berner, Sam Mc-
Candlish, Alec Radford, Ilya Sutskever, and Dario
Amodei. 2020b. Language models are few-shot
learners. ArXiv, abs/2005.14165.
Nicholas Carlini, Florian Tramer, Eric Wallace,
Matthew Jagielski, Ariel Herbert-Voss, Katherine
Lee, Adam Roberts, Tom Brown, Dawn Song, Ul-
far Erlingsson, Alina Oprea, and Colin Raffel. 2021.
Extracting training data from large language mod-
els. In Proceedings of USENIX Security Symposium,
pages 2633–2650.
Chen Chen, Jie Fu, and L. Lyu. 2023. A pathway
towards responsible ai generated content. ArXiv,
abs/2303.01325. | Multi-step Jailbreaking Privacy Attacks on ChatGPT |
[77] Shukang Yin, Chaoyou Fu, Sirui Zhao, Ke Li, Xing
A Survey on
arXiv preprint
Sun, Tong Xu, and Enhong Chen.
Multimodal Large Language Models.
arXiv:2306.13549, 2023. 2
[78] Xiaohua Zhai, Alexander Kolesnikov, Neil Houlsby, and
Lucas Beyer. Scaling Vision Transformers. In Proceed-
ings of the IEEE/CVF Conference on Computer Vision and
Pattern Recognition, pages 12104–12113, 2022. 3
[79] Pan Zhang, Xiaoyi Dong Bin Wang, Yuhang Cao, Chao
Xu, Linke Ouyang, Zhiyuan Zhao, Shuangrui Ding,
Songyang Zhang, Haodong Duan, Hang Yan, et al.
InternLM-XComposer: A Vision-Language Large Model
for Advanced Text-image Comprehension and Composi-
tion. arXiv preprint arXiv:2309.15112, 2023. 3
[80] Runbang Zhang, Yixiao Zhang, Kai Shao, Ying Shan, and
Gus Xia. Vis2Mus: Exploring Multimodal Representa-
tion Mapping for Controllable Music Generation. arXiv
preprint arXiv:2211.05543, 2022. 2, 3 | M2UGen |
incorporated into the next prompt, which provides
the agent with a form of memory. This meticulous
approach enhances the reliability and interpretabil-
ity of the agent’s actions, thereby facilitating more
informed decision-making. The deployment phase
stops when the agent determines that the task has
been accomplished, at which point it can exit the
process by taking the Exit() action. | AppAgents |
Please help me fix this.
Length = 5, Number of vowels = 2
John was sitting on the floor in front of his laptop when he heard a
knock at the door. He opened it to find a dog standing there. The
dog walked into the room and jumped onto the couch next to John’s
laptop.
Hi [Friend],
I’m having a bit of trouble with my current job. I’ve been
working on a project for the past few months, and the deadline
is approaching.
My manager has asked me to stay late every day this
week to finish it. I don’t think I can do that because I have other
commitments.
What should I do?
– Use reusable water bottles instead of disposable plastic ones.
– Turn off lights when you leave a room.
– Recycle paper, cans and bottles.
– Plant trees to absorb carbon dioxide. | SELF-INSTRUCT- Aligning Language Model with Self Generated Instructions |
Yarn feeder 2, pixel-by-pixel rather different.
This sort of thing is not naturally represented in current neural network approaches.
For one thing, doing so would presumably require a prior notion of an object, which
itself is not readily represented in most current neural networks. Yet such knowledge
needs to be a central part of robust AI, and we must both have strategies for acquiring
that knowledge and architectures that can acquire, represent, and manipulate that
knowledge -- some of which appears to be symbolic in nature.
In principle, perhaps a smaller subset of the axioms we proposed could be innate,
others learned, though as yet I know of no system capable of learning axioms of this
sort. (Here again, neurosymbolic systems with embedded knowledge might be helpful.)
Importantly, the set of frameworks themselves cluster into a fairly recognizable set of
domains, such as knowledge about space, time, and causality, as sketched here:
31 | The Next Decade in AI- |
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‘‘Why should I trust you?" Explaining
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arXiv:2004.05150 [cs], December 2020. URL http://arxiv.org/abs/2004.05150. | CRAMMING-TRAININGALANGUAGEMODELONA SINGLEGPUINONEDAY |
3.6 Ablation Study on Pretraining Modalities
Figure 5: The zero-shot performance of pre-trained
BiomedGPT and OFA with different model scales.
Here, the model sizes are denoted by ‘L’, ‘B’, ‘M’,
and ‘S’, which stand for large-, base-, medium-, and
small-sized models, respectively.
This section addresses the query: “Can the proposed model handle unseen data modalities (e.g., images
from a new different imaging device like an ultrasound)?” To investigate this, we have adjusted our dataset
10
Table 5: Ablation study on holding out task groups. All the results are obtained from the small-scale model.
Model
OursSmall
w/o MLM
w/o MIM
w/o OD
w/o PTB
Pneumonia ROCO VQA-RAD MeQSum MedNLI
91.8
87.0
88.3
88.3
88.9
13.2
12.0
12.2
12.7
6.8
37.5
32.4
33.5
37.7
2.5
42.2
19.1
44.3
44.8
46.6
69.3
68.6
69.9
68.2
72.6 | BiomedGPT |
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e
p
y
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mec
cat
mec
mec
mec
mec
cat
cat
mec
mec
mec
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cat
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int | Knowledge graphs as tools for explainable machine learning: A survey |
abilities are also applicable to multi-modal models, which could be the foundation of GPT-4’s
impressive visual description capabilities.
To substantiate our hypothesis, we present a novel model named MiniGPT-4. It utilizes an advanced
large language model (LLM), Vicuna [8], which is built upon LLaMA [32] and reported to achieve
90% of ChatGPT’s quality as per GPT-4’s evaluation, as the language decoder. In terms of visual
perception, we employ the same pretrained vision component of BLIP-2 [16] that consists of a
ViT-G/14 from EVA-CLIP [13] and a Q-Former. MiniGPT-4 adds a single projection layer to align
the encoded visual features with the Vicuna language model and freezes all the other vision and
language components. MiniGPT-4 is initially trained for 20k steps using a batch size of 256 on 4
A100 GPUs, leveraging a combined dataset that includes images from LAION [26], Conceptual
Captions [5, 27], and SBU [20] to align visual features with the Vicuna language model. However, | MiniGPT-4- Enhancing Vision-Language Understanding with Advanced Large Language Models |
3.1 Multi-modal Feature Encoders
In order to accomplish multi-modal music understanding
and generation, the M2UGen model needs to be able to
process multi-modal inputs. To achieve this, it incor-
porates frozen pre-trained encoders capable of compre-
hending various modalities, including music, images, and
videos. These encoders extract relevant information to ad-
dress tasks associated with music question answering and
music generation within the M2UGen framework. Specif-
ically, the following encoders are employed for processing
involved modalities:
MERT Encoder Notably, the MERT model [41] has
demonstrated exceptional performance in downstream
music tagging tasks, as highlighted by Liu et al. (2023)
[47], surpassing alternative models such as Wav2CLIP
3
M2UGen
A PREPRINT | M2UGen |
offer a blueprint to imitate: a large-scale field experiment where exposure to
social media messages is randomly assigned, which allows the authors to
make valid causal claims. Ethical considerations should also be part of this
debate, however, particularly as scholars focus their efforts on studying
how extremism fueled by social media interactions can lead to offline
violence. | Social_Media_and_Democracy |
Likelihood models like our text-to-image diffusion models have a notorious tendency to overfit to distributional
regularities in the dataset. For example, a text-to-image model that is trained on text that always starts with a
space character will not work properly if you try to perform inference with prompts that do not also start with
that space.
When it comes to training on synthetic captions, we need to consider this issue. Our captioner model could
have many modal behaviors that are difficult to detect, but which will become biases of our text-to-image
model if it is trained on those captions. Examples of where this might occur is in letter casing, where
punctuation appears in the caption (e.g. does it always end with a period?), how long the captions are, or
stylistic tendencies such as starting all captions with the words "a" or "an".
The best way to overcome this issue is to regularize our inputs to a distribution of text that is closer to the style | Improving Image Generation with Better Captions |
pipelines to scale weakly supervised speech recognition
to 10,000 and 30,000 hours of noisier training data. This
trade-off between quality and quantity is often the right
call. Although understudied so far for speech recognition,
recent work in computer vision has demonstrated that mov-
ing beyond gold-standard crowdsourced datasets such as
ImageNet (Russakovsky et al., 2015) to much larger but
weakly supervised datasets significantly improves the ro-
bustness and generalization of models (Mahajan et al., 2018;
Kolesnikov et al., 2020).
Yet these new datasets are only a few times larger than the
sum of existing high-quality datasets and still much smaller
than prior unsupervised work. In this work we close that
gap, scaling weakly supervised speech recognition the next
order of magnitude to 680,000 hours of labeled audio data.
We call our approach Whisper2. We demonstrate models
trained at this scale transfer well to existing datasets zero- | RobustSpeechRecognitionviaLarge-ScaleWeakSupervision |
[312] Peng, Z., W. Wang, L. Dong, et al. Kosmos-2: Grounding multimodal large language models
to the world. CoRR, abs/2306.14824, 2023.
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The case of Christie’s Belamy auction revealed many issues regarding the questions of authorship and copyright, as
well as raised general discussions on the ethical considerations that have to be taken into account during production,
promotion and sale of an AI artwork. In the case of the aforementioned auction, the artwork was presented as being
autonomously produced by an AI system, yet the authors that created that system, nor the author of the code that was
used to run the network, did not receive any formal acknowledgement. When an AI artworks gets sold for such an
unexpectedly large price, who holds the right to profit from the sale becomes a very relevant question and triggers
many discussions. McCormack et al. [90] provide a detailed overview of the problematic aspects of the “Portrait of
Edmond Belamy” regarding authorship, authenticity and other important aspects of AI Art. Epstein et al. [43] use the | UNDERSTANDINGANDCREATINGARTWITHAI-REVIEWAND OUTLOOK |
After generating the dataset, we fine-tune sev-
eral smaller language models with varying sizes
(from 61M to 1.5B) and architectures (encoder-
decoder and decoder-only). Furthermore, we com-
pare different variations of models with the same
architecture. Our work is also distinguished from
previous research by providing a comprehensive
evaluation of the resulting models. We assess the
performance of the models on various NLP down-
stream tasks, in addition to manual human eval-
uation of the model’s outputs. This analysis of-
fers a more in-depth understanding of the models’
strengths and weaknesses.
Our contributions can be summarized as follows:
1. We release a large-scale instruction dataset
that contains over 2.58M examples. To the
best of our knowledge, this dataset is the
largest instruction dataset currently available
in the NLP literature. Our instruction dataset
is ×50 larger than the one released by Taori
et al. (2023). | LaMini-LM- A Diverse Herd of Distilled Models from Large-Scale Instructions |
After comparing hash embeddings with traditional embeddings, we turn to evaluating the contri-
bution of the orthographic features. We start with spaCy’s default NORM, PREFIX, SUFFIX and
SHAPE features, then gradually remove them one-by-one while measuring their effect on perfor-
mance. We also included an ORTH-only configuration, which represents the most common method
outside of spaCy. Tables 2 and 3 report the relative error increase in the F1-score for Dutch CoNLL
2002 and AnEM. We used these two datasets because CoNLL is a standard benchmark represent-
ing a common choice to tune default parameters and architectures. In contrast, AnEM is a smaller
dataset with a specialized domain.
Table 2 reports the results for CoNLL Dutch, which are in line with our expectations: removing any
of the features degrades performance and ORTH performs the worst overall. We do find the same
pattern for the AnEM dataset in Table 3 but only if we consider the global F1 score. However, when | MULTI HASH EMBEDDINGS IN SPACY |
However, some scholars caution about the potential for social media to
undermine the correction of misinformation. The “social” nature of social
media may increase levels of exposure to misinformation, as individuals are
more likely to read news that has been shared or endorsed by members of
their social networks (Messing and Westwood 2014; Anspach 2017). The
nature of the social media environment may also inhibit corrections of
misinformation; Jun, Meng, and Johar (2017) warn that people are less
likely to fact-check statements in social settings – a form of “virtual
bystander effect.” Furthermore, even if corrections circulate on social
media, individuals may be more attentive to user comments on these posts
than to the actual fact-checking messages themselves. If these comments
distort or otherwise misrepresent corrections, individuals may not become
better informed, despite their exposure to fact-checking information
(Anspach and Carlson 2018).
some
scholars
deployed | Social_Media_and_Democracy |
Subsets and Splits