- See Also
-
Links
- “VisualWebBench: How Far Have Multimodal LLMs Evolved in Web Page Understanding and Grounding?”, Liu et al 2024
- “How Tech Giants Cut Corners to Harvest Data for AI: OpenAI, Google and Meta Ignored Corporate Policies, Altered Their Own Rules and Discussed Skirting Copyright Law As They Sought Online Information to Train Their Newest Artificial Intelligence Systems”, Metz et al 2024
- “Vulnerability Detection With Code Language Models: How Far Are We?”, Ding et al 2024
- “COIG-CQIA: Quality Is All You Need for Chinese Instruction Fine-Tuning”, Bai et al 2024
- “RewardBench: Evaluating Reward Models for Language Modeling”, Lambert et al 2024
- “Evaluating Text to Image Synthesis: Survey and Taxonomy of Image Quality Metrics”, Hartwig et al 2024
- “Hierarchical Feature Warping and Blending for Talking Head Animation”, Zhang et al 2024
- “Mastering Text, Code and Math Simultaneously via Fusing Highly Specialized Language Models”, Ding et al 2024
- “ELLA: Equip Diffusion Models With LLM for Enhanced Semantic Alignment”, Hu et al 2024
- “Hal-Eval: A Universal and Fine-Grained Hallucination Evaluation Framework for Large Vision Language Models”, Jiang et al 2024
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ArtPrompt: ASCII Art-Based Jailbreak Attacks against Aligned LLMs”, Jiang et al 2024 - “AnimeDiffusion: Anime Diffusion Colorization”, Cao et al 2024
- “I Am a Strange Dataset: Metalinguistic Tests for Language Models”, Thrush et al 2024
- “Generative AI for Math: Part I—MathPile: A Billion-Token-Scale Pretraining Corpus for Math”, Wang et al 2023
- “WaveCoder: Widespread And Versatile Enhanced Instruction Tuning With Refined Data Generation”, Yu et al 2023
- “StarVector: Generating Scalable Vector Graphics Code from Images”, Rodriguez et al 2023
- “Rich Human Feedback for Text-To-Image Generation”, Liang et al 2023
- “TinyGSM: Achieving >80% on GSM8k With Small Language Models”, Liu et al 2023
- “Retrieving Conditions from Reference Images for Diffusion Models”, Tang et al 2023
- “Sequential Modeling Enables Scalable Learning for Large Vision Models”, Bai et al 2023
- “BioCLIP: A Vision Foundation Model for the Tree of Life”, Stevens et al 2023
- “Efficient Transformer Knowledge Distillation: A Performance Review”, Brown et al 2023
- “GPQA: A Graduate-Level Google-Proof Q&A Benchmark”, Rein et al 2023
- “Instruction-Following Evaluation for Large Language Models”, Zhou et al 2023
- “AnyText: Multilingual Visual Text Generation And Editing”, Tuo et al 2023
- “GLaMM: Pixel Grounding Large Multimodal Model”, Rasheed et al 2023
- “Don’t Make Your LLM an Evaluation Benchmark Cheater”, Zhou et al 2023
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- “Ignore This Title and HackAPrompt: Exposing Systemic Vulnerabilities of LLMs through a Global Scale Prompt Hacking Competition”, Schulhoff et al 2023
- “From Scarcity to Efficiency: Improving CLIP Training via Visual-Enriched Captions”, Lai et al 2023
- “TabLib: A Dataset of 627M Tables With Context”, Eggert et al 2023
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- “OpenWebMath: An Open Dataset of High-Quality Mathematical Web Text”, Paster et al 2023
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- “GoodWiki”, Choi 2023
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- “The ConceptARC Benchmark: Evaluating Understanding and Generalization in the ARC Domain”, Moskvichev et al 2023
- “Android in the Wild: A Large-Scale Dataset for Android Device Control”, Rawles et al 2023
- “DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI”, Zhang et al 2023
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- “Instruction Mining: High-Quality Instruction Data Selection for Large Language Models”, Cao et al 2023
- “Test-Time Training on Video Streams”, Wang et al 2023
- “HEADLINES: A Massive Scale Semantic Similarity Dataset of Historical English”, Silcock & Dell 2023
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- “ChessGPT: Bridging Policy Learning and Language Modeling”, Feng et al 2023
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- “The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora With Web Data, and Web Data Only”, Penedo et al 2023
- “Let’s Verify Step by Step”, Lightman et al 2023
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- “C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models”, Huang et al 2023
- “TinyStories: How Small Can Language Models Be and Still Speak Coherent English?”, Eldan & Li 2023
- “Pick-A-Pic: An Open Dataset of User Preferences for Text-To-Image Generation”, Kirstain et al 2023
- “LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions”, Wu et al 2023
- “Multi-Party Chat (MultiLIGHT): Conversational Agents in Group Settings With Humans and Models”, Wei et al 2023
- “ImageNet-Hard: The Hardest Images Remaining from a Study of the Power of Zoom and Spatial Biases in Image Classification”, Taesiri et al 2023
- “Parsing-Conditioned Anime Translation: A New Dataset and Method”, Li et al 2023c
- “Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling”, Biderman et al 2023
- “Abstraction-Perception Preserving Cartoon Face Synthesis”, Ho et al 2023
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- “The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset”, Laurençon et al 2023
- “Large Language Models Are State-Of-The-Art Evaluators of Translation Quality”, Kocmi & Federmann 2023
- “Benchmarks for Automated Commonsense Reasoning: A Survey”, Davis 2023
- “Off-The-Grid MARL (OG-MARL): Datasets With Baselines for Offline Multi-Agent Reinforcement Learning”, Formanek et al 2023
- “The BabyLM Challenge: Sample-Efficient Pretraining on a Developmentally Plausible Corpus”, Warstadt et al 2023
- “The Semantic Scholar Open Data Platform”, Kinney et al 2023
- “Interactive-Chain-Prompting (INTERCPT): Ambiguity Resolution for Crosslingual Conditional Generation With Interaction”, Pilault et al 2023
- “How Close Is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection”, Guo et al 2023
- “Med-PaLM: Large Language Models Encode Clinical Knowledge”, Singhal et al 2022
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- “HALIE: Evaluating Human-Language Model Interaction”, Lee et al 2022
- “A Whack-A-Mole Dilemma: Shortcuts Come in Multiples Where Mitigating One Amplifies Others”, Li et al 2022
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- “UniSumm: Unified Few-Shot Summarization With Multi-Task Pre-Training and Prefix-Tuning”, Chen et al 2022
- “A Creative Industry Image Generation Dataset Based on Captions”, Yuejia et al 2022
- “AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities”, Chen et al 2022
- “AnimeRun: 2D Animation Visual Correspondence from Open Source 3D Movies”, Siyao et al 2022
- “MMDialog: A Large-Scale Multi-Turn Dialogue Dataset Towards Multi-Modal Open-Domain Conversation”, Feng et al 2022
- “BLOOMZ/mT0: Crosslingual Generalization through Multitask Finetuning”, Muennighoff et al 2022
- “Dungeons and Data: A Large-Scale NetHack Dataset”, Hambro et al 2022
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- “Large Language Models Can Self-Improve”, Huang et al 2022
- “CARP: Robust Preference Learning for Storytelling via Contrastive Reinforcement Learning”, Castricato et al 2022
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- “Most Language Models Can Be Poets Too: An AI Writing Assistant and Constrained Text Generation Studio”, Roush et al 2022
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- “FOLIO: Natural Language Reasoning With First-Order Logic”, Han et al 2022
- “Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned”, Ganguli et al 2022
- “Bugs in the Data: How ImageNet Misrepresents Biodiversity”, Luccioni & Rolnick 2022
- “Discovering Bugs in Vision Models Using Off-The-Shelf Image Generation and Captioning”, Wiles et al 2022
- “Benchmarking Compositionality With Formal Languages”, Valvoda et al 2022
- “Quality Not Quantity: On the Interaction between Dataset Design and Robustness of CLIP”, Nguyen et al 2022
- “Learning to Generalize With Object-Centric Agents in the Open World Survival Game Crafter”, Stanić et al 2022
- “Few-Shot Adaptation Works With UnpredicTable Data”, Chan et al 2022
- “Language Models Can Teach Themselves to Program Better”, Haluptzok et al 2022
- “RealTime QA: What’s the Answer Right Now?”, Kasai et al 2022
- “NewsStories: Illustrating Articles With Visual Summaries”, Tan et al 2022
- “CelebV-HQ: A Large-Scale Video Facial Attributes Dataset”, Zhu et al 2022
- “Why Do Tree-Based Models Still Outperform Deep Learning on Tabular Data?”, Grinsztajn et al 2022
- “Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset”, Henderson et al 2022
- “Forecasting Future World Events With Neural Networks”, Zou et al 2022
- “RST: ReStructured Pre-Training”, Yuan & Liu 2022
- “Learning to Generate Artistic Character Line Drawing”, Fang et al 2022
- “Dataset Condensation via Efficient Synthetic-Data Parameterization”, Kim et al 2022
- “Bongard-HOI: Benchmarking Few-Shot Visual Reasoning for Human-Object Interactions”, Jiang et al 2022
- “Fine-Grained Image Captioning With CLIP Reward”, Cho et al 2022
- “FLEURS: Few-Shot Learning Evaluation of Universal Representations of Speech”, Conneau et al 2022
- “InstructDial: Improving Zero and Few-Shot Generalization in Dialogue through Instruction Tuning”, Gupta et al 2022
- “Learning to Model Editing Processes”, Reid & Neubig 2022
- “Flexible Diffusion Modeling of Long Videos”, Harvey et al 2022
- “Housekeep: Tidying Virtual Households Using Commonsense Reasoning”, Kant et al 2022
- “Instruction Induction: From Few Examples to Natural Language Task Descriptions”, Honovich et al 2022
- “Down and Across: Introducing Crossword-Solving As a New NLP Benchmark”, Kulshreshtha et al 2022
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- “Dialog Inpainting: Turning Documents into Dialogues”, Dai et al 2022
- “SymphonyNet: Symphony Generation With Permutation Invariant Language Model”, Liu et al 2022
- “Building Machine Translation Systems for the Next Thousand Languages”, Bapna et al 2022
- “When Does Dough Become a Bagel? Analyzing the Remaining Mistakes on ImageNet”, Vasudevan et al 2022
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- “A Challenging Benchmark of Anime Style Recognition”, Li et al 2022
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- “Winoground: Probing Vision and Language Models for Visio-Linguistic Compositionality”, Thrush et al 2022
- “KNN-Diffusion: Image Generation via Large-Scale Retrieval”, Ashual et al 2022
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- “STaR: Bootstrapping Reasoning With Reasoning”, Zelikman et al 2022
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- “Bamboo: Building Mega-Scale Vision Dataset Continually With Human-Machine Synergy”, Zhang et al 2022
- “Self-Distilled StyleGAN: Towards Generation from Internet Photos”, Mokady et al 2022
- “RuCLIP—New Models and Experiments: a Technical Report”, Shonenkov et al 2022
- “Wukong: 100 Million Large-Scale Chinese Cross-Modal Pre-Training Dataset and A Foundation Framework”, Gu et al 2022
- “ROME: Locating and Editing Factual Associations in GPT”, Meng et al 2022
- “DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-To-Image Generative Transformers”, Cho et al 2022
- “PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts”, Bach et al 2022
- “StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets”, Sauer et al 2022
- “BLIP: Bootstrapping Language-Image Pre-Training for Unified Vision-Language Understanding and Generation”, Li et al 2022
- “Can Wikipedia Help Offline Reinforcement Learning?”, Reid et al 2022
- “SWAG: Revisiting Weakly Supervised Pre-Training of Visual Perception Models”, Singh et al 2022
- “CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities”, Lee et al 2022
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- “GLIDE: Towards Photorealistic Image Generation and Editing With Text-Guided Diffusion Models”, Nichol et al 2021
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- “WebGPT: Browser-Assisted Question-Answering With Human Feedback”, Nakano et al 2021
- “GLaM: Efficient Scaling of Language Models With Mixture-Of-Experts”, Du et al 2021
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- “BASIC: Combined Scaling for Open-Vocabulary Image Classification”, Pham et al 2021
- “Solving Probability and Statistics Problems by Program Synthesis”, Tang et al 2021
- “Few-Shot Self-Rationalization With Natural Language Prompts”, Marasović et al 2021
- “AnimeCeleb: Large-Scale Animation CelebHeads Dataset for Head Reenactment”, Kim et al 2021
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- “An Explanation of In-Context Learning As Implicit Bayesian Inference”, Xie et al 2021
- “LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs”, Schuhmann et al 2021
- “Training Verifiers to Solve Math Word Problems”, Cobbe et al 2021
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- “HTCN: Harmonious Text Colorization Network for Visual-Textual Presentation Design”, Yang et al 2021c
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- “Can Machines Learn Morality? The Delphi Experiment”, Jiang et al 2021
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- “LAION-400-Million Open Dataset”, Schuhmann 2021
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- “Scaling Vision Transformers”, Zhai et al 2021
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- “BEIR: A Heterogenous Benchmark for Zero-Shot Evaluation of Information Retrieval Models”, Thakur et al 2021
- “SpeechStew: Simply Mix All Available Speech Recognition Data to Train One Large Neural Network”, Chan et al 2021
- “Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks”, Northcutt et al 2021
- “NaturalProofs: Mathematical Theorem Proving in Natural Language”, Welleck et al 2021
- “Get Your Vitamin C! Robust Fact Verification With Contrastive Evidence (VitaminC)”, Schuster et al 2021
- “Are NLP Models Really Able to Solve Simple Math Word Problems?”, Patel et al 2021
- “Measuring Mathematical Problem Solving With the MATH Dataset”, Hendrycks et al 2021
- “WIT: Wikipedia-Based Image Text Dataset for Multimodal Multilingual Machine Learning”, Srinivasan et al 2021
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- “Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts”, Changpinyo et al 2021
- “ALIGN: Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision”, Jia et al 2021
- “Mind the Gap: Assessing Temporal Generalization in Neural Language Models § Scaling”, Lazaridou et al 2021
- “Scaling Laws for Transfer”, Hernandez et al 2021
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- “CLIP: Learning Transferable Visual Models From Natural Language Supervision”, Radford et al 2021
- “CLIP: Connecting Text and Images: We’re Introducing a Neural Network Called CLIP Which Efficiently Learns Visual Concepts from Natural Language Supervision. CLIP Can Be Applied to Any Visual Classification Benchmark by Simply Providing the Names of the Visual Categories to Be Recognized, Similar to the ‘Zero-Shot’ Capabilities of GPT-2 and GPT-3”, Radford et al 2021
- “The Pile: An 800GB Dataset of Diverse Text for Language Modeling”, Gao et al 2021
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- “Restoring Ancient Text Using Deep Learning (Pythia): a Case Study on Greek Epigraphy”, Assael et al 2019
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- “OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge”, Marino et al 2019
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- “SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems”, Wang et al 2019
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- “ProductNet: a Collection of High-Quality Datasets for Product Representation Learning”, Wang et al 2019
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- “Atari-HEAD: Atari Human Eye-Tracking and Demonstration Dataset”, Zhang et al 2019
- “LIGHT: Learning to Speak and Act in a Fantasy Text Adventure Game”, Urbanek et al 2019
- “A Replication Study: Machine Learning Models Are Capable of Predicting Sexual Orientation From Facial Images”, Leuner 2019
- “Language Models Are Unsupervised Multitask Learners”, Radford et al 2019
- “The Omniglot Challenge: a 3-Year Progress Report”, Lake et al 2019
- “Do We Train on Test Data? Purging CIFAR of Near-Duplicates”, Barz & Denzler 2019
- “The RobotriX: An EXtremely Photorealistic and Very-Large-Scale Indoor Dataset of Sequences With Robot Trajectories and Interactions”, Garcia-Garcia et al 2019
- “FIGR: Few-Shot Image Generation With Reptile”, Clouâtre & Demers 2019
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- “HotpotQA: A Dataset for Diverse, Explainable Multi-Hop Question Answering”, Yang et al 2018
- “Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization”, Narayan et al 2018
- “CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images”, Guo et al 2018
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- “FEVER: a Large-Scale Dataset for Fact Extraction and VERification”, Thorne et al 2018
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- “The Kinetics Human Action Video Dataset”, Kay et al 2017
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- “MS MARCO: A Human Generated MAchine Reading COmprehension Dataset”, Bajaj et al 2016
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- “Pointer Sentinel Mixture Models”, Merity et al 2016
- “Solving General Arithmetic Word Problems”, Roy & Roth 2016
- “The LAMBADA Dataset: Word Prediction Requiring a Broad Discourse Context”, Paperno et al 2016
- “SQuAD: 100,000+ Questions for Machine Comprehension of Text”, Rajpurkar et al 2016
- “Convolutional Sketch Inversion”, Güçlütürk et al 2016
- “The MovieLens Datasets: History and Context”, Harper & Konstan 2015
- “Neural Module Networks”, Andreas et al 2015
- “Sketch-Based Manga Retrieval Using Manga109 Dataset”, Matsui et al 2015
- “Amazon Reviews: Image-Based Recommendations on Styles and Substitutes”, McAuley et al 2015
- “Teaching Machines to Read and Comprehend”, Hermann et al 2015
- “LSUN: Construction of a Large-Scale Image Dataset Using Deep Learning With Humans in the Loop”, Yu et al 2015
- “VQA: Visual Question Answering”, Agrawal et al 2015
- “YFCC100M: The New Data in Multimedia Research”, Thomee et al 2015
- “ImageNet Large Scale Visual Recognition Challenge”, Russakovsky et al 2014
- “Microsoft COCO: Common Objects in Context”, Lin et al 2014
- “N-Gram Counts and Language Models from the Common Crawl”, Buck et al 2014
- “Ukiyo-E Search”, Resig 2013
- “UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild”, Soomro et al 2012
- “The Caltech-UCSD Birds-200-2011 Dataset”, Wah et al 2011
- “Unbiased Look at Dataset Bias”, Torralba & Efros 2011
- “Caltech-UCSD Birds 200”, Welinder et al 2010
- “Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments”, Huang et al 2008
- “Building a Large Annotated Corpus of English: The Penn Treebank”, Marcus et al 1993
- “Solving Math Word Problems: We’ve Trained a System That Solves Grade School Math Problems With Nearly Twice the Accuracy of a Fine-Tuned GPT-3 Model. It Solves about 90% As Many Problems As Real Kids: a Small Sample of 9-12 Year Olds Scored 60% on a Test from Our Dataset, While Our System Scored 55% on Those Same Problems. This Is Important Because Today’s AI Is Still Quite Weak at Commonsense Multistep Reasoning, Which Is Easy Even for Grade School Kids. We Achieved These Results by Training Our Model to Recognize Its Mistakes, so That It Can Try Repeatedly Until It Finds a Solution That Works”
- Sort By Magic
- Wikipedia
- Miscellaneous
- Link Bibliography
See Also
Links
“VisualWebBench: How Far Have Multimodal LLMs Evolved in Web Page Understanding and Grounding?”, Liu et al 2024
VisualWebBench: How Far Have Multimodal LLMs Evolved in Web Page Understanding and Grounding?
“How Tech Giants Cut Corners to Harvest Data for AI: OpenAI, Google and Meta Ignored Corporate Policies, Altered Their Own Rules and Discussed Skirting Copyright Law As They Sought Online Information to Train Their Newest Artificial Intelligence Systems”, Metz et al 2024
“Vulnerability Detection With Code Language Models: How Far Are We?”, Ding et al 2024
Vulnerability Detection with Code Language Models: How Far Are We?
“COIG-CQIA: Quality Is All You Need for Chinese Instruction Fine-Tuning”, Bai et al 2024
COIG-CQIA: Quality is All You Need for Chinese Instruction Fine-tuning
“RewardBench: Evaluating Reward Models for Language Modeling”, Lambert et al 2024
“Evaluating Text to Image Synthesis: Survey and Taxonomy of Image Quality Metrics”, Hartwig et al 2024
Evaluating Text to Image Synthesis: Survey and Taxonomy of Image Quality Metrics
“Hierarchical Feature Warping and Blending for Talking Head Animation”, Zhang et al 2024
Hierarchical Feature Warping and Blending for Talking Head Animation
“Mastering Text, Code and Math Simultaneously via Fusing Highly Specialized Language Models”, Ding et al 2024
Mastering Text, Code and Math Simultaneously via Fusing Highly Specialized Language Models
“ELLA: Equip Diffusion Models With LLM for Enhanced Semantic Alignment”, Hu et al 2024
ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment
“Hal-Eval: A Universal and Fine-Grained Hallucination Evaluation Framework for Large Vision Language Models”, Jiang et al 2024
“ArtPrompt: ASCII Art-Based Jailbreak Attacks against Aligned LLMs”, Jiang et al 2024
ArtPrompt: ASCII Art-based Jailbreak Attacks against Aligned LLMs
“AnimeDiffusion: Anime Diffusion Colorization”, Cao et al 2024
“I Am a Strange Dataset: Metalinguistic Tests for Language Models”, Thrush et al 2024
I am a Strange Dataset: Metalinguistic Tests for Language Models
“Generative AI for Math: Part I—MathPile: A Billion-Token-Scale Pretraining Corpus for Math”, Wang et al 2023
Generative AI for Math: Part I—MathPile: A Billion-Token-Scale Pretraining Corpus for Math
“WaveCoder: Widespread And Versatile Enhanced Instruction Tuning With Refined Data Generation”, Yu et al 2023
WaveCoder: Widespread And Versatile Enhanced Instruction Tuning with Refined Data Generation
“StarVector: Generating Scalable Vector Graphics Code from Images”, Rodriguez et al 2023
StarVector: Generating Scalable Vector Graphics Code from Images
“Rich Human Feedback for Text-To-Image Generation”, Liang et al 2023
“TinyGSM: Achieving >80% on GSM8k With Small Language Models”, Liu et al 2023
“Retrieving Conditions from Reference Images for Diffusion Models”, Tang et al 2023
Retrieving Conditions from Reference Images for Diffusion Models
“Sequential Modeling Enables Scalable Learning for Large Vision Models”, Bai et al 2023
Sequential Modeling Enables Scalable Learning for Large Vision Models
“BioCLIP: A Vision Foundation Model for the Tree of Life”, Stevens et al 2023
“Efficient Transformer Knowledge Distillation: A Performance Review”, Brown et al 2023
Efficient Transformer Knowledge Distillation: A Performance Review
“GPQA: A Graduate-Level Google-Proof Q&A Benchmark”, Rein et al 2023
“Instruction-Following Evaluation for Large Language Models”, Zhou et al 2023
“AnyText: Multilingual Visual Text Generation And Editing”, Tuo et al 2023
“GLaMM: Pixel Grounding Large Multimodal Model”, Rasheed et al 2023
“Don’t Make Your LLM an Evaluation Benchmark Cheater”, Zhou et al 2023
“CommonCanvas: An Open Diffusion Model Trained With Creative-Commons Images”, Gokaslan et al 2023
CommonCanvas: An Open Diffusion Model Trained with Creative-Commons Images
“Ignore This Title and HackAPrompt: Exposing Systemic Vulnerabilities of LLMs through a Global Scale Prompt Hacking Competition”, Schulhoff et al 2023
“From Scarcity to Efficiency: Improving CLIP Training via Visual-Enriched Captions”, Lai et al 2023
From Scarcity to Efficiency: Improving CLIP Training via Visual-enriched Captions
“TabLib: A Dataset of 627M Tables With Context”, Eggert et al 2023
“SWE-Bench: Can Language Models Resolve Real-World GitHub Issues?”, Jimenez et al 2023
SWE-bench: Can Language Models Resolve Real-World GitHub Issues?
“OpenWebMath: An Open Dataset of High-Quality Mathematical Web Text”, Paster et al 2023
OpenWebMath: An Open Dataset of High-Quality Mathematical Web Text
“FreshLLMs: Refreshing Large Language Models With Search Engine Augmentation”, Vu et al 2023
FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation
“UltraFeedback: Boosting Language Models With High-Quality Feedback”, Cui et al 2023
UltraFeedback: Boosting Language Models with High-quality Feedback
“Demystifying CLIP Data”, Xu et al 2023
“The Cambridge Law Corpus: A Corpus for Legal AI Research”, Östling et al 2023
“LongLoRA: Efficient Fine-Tuning of Long-Context Large Language Models”, Chen et al 2023
LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models
“SlimPajama-DC: Understanding Data Combinations for LLM Training”, Shen et al 2023
SlimPajama-DC: Understanding Data Combinations for LLM Training
“MADLAD-400: A Multilingual And Document-Level Large Audited Dataset”, Kudugunta et al 2023
MADLAD-400: A Multilingual And Document-Level Large Audited Dataset
“GoodWiki”, Choi 2023
“From Sparse to Dense: GPT-4 Summarization With Chain of Density (CoD) Prompting”, Adams et al 2023
From Sparse to Dense: GPT-4 Summarization with Chain of Density (CoD) Prompting
“American Stories: A Large-Scale Structured Text Dataset of Historical U.S. Newspapers”, Dell et al 2023
American Stories: A Large-Scale Structured Text Dataset of Historical U.S. Newspapers
“The ConceptARC Benchmark: Evaluating Understanding and Generalization in the ARC Domain”, Moskvichev et al 2023
The ConceptARC Benchmark: Evaluating Understanding and Generalization in the ARC Domain
“Android in the Wild: A Large-Scale Dataset for Android Device Control”, Rawles et al 2023
Android in the Wild: A Large-Scale Dataset for Android Device Control
“DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI”, Zhang et al 2023
DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI
“AlpaGasus: Training A Better Alpaca With Fewer Data”, Chen et al 2023
“InternVid: A Large-Scale Video-Text Dataset for Multimodal Understanding and Generation”, Wang et al 2023
InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation
“Instruction Mining: High-Quality Instruction Data Selection for Large Language Models”, Cao et al 2023
Instruction Mining: High-Quality Instruction Data Selection for Large Language Models
“Test-Time Training on Video Streams”, Wang et al 2023
“HEADLINES: A Massive Scale Semantic Similarity Dataset of Historical English”, Silcock & Dell 2023
HEADLINES: A Massive Scale Semantic Similarity Dataset of Historical English
“LeanDojo: Theorem Proving With Retrieval-Augmented Language Models”, Yang et al 2023
LeanDojo: Theorem Proving with Retrieval-Augmented Language Models
“SugarCrepe: Fixing Hackable Benchmarks for Vision-Language Compositionality”, Hsieh et al 2023
SugarCrepe: Fixing Hackable Benchmarks for Vision-Language Compositionality
“ARIES: A Corpus of Scientific Paper Edits Made in Response to Peer Reviews”, D’Arcy et al 2023
ARIES: A Corpus of Scientific Paper Edits Made in Response to Peer Reviews
“OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents”, Laurençon et al 2023
OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents
“AI Is a Lot of Work: As the Technology Becomes Ubiquitous, a Vast Tasker Underclass Is Emerging—And Not Going Anywhere”, Dzieza 2023
“Anime Character Identification and Tag Prediction by Multimodality Modeling: Dataset and Model”, Yi et al 2023
Anime Character Identification and Tag Prediction by Multimodality Modeling: Dataset and Model
“ChessGPT: Bridging Policy Learning and Language Modeling”, Feng et al 2023
“Why YouTube Could Give Google an Edge in AI”, Victor 2023
“Artificial Artificial Artificial Intelligence: Crowd Workers Widely Use Large Language Models for Text Production Tasks”, Veselovsky et al 2023
“The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora With Web Data, and Web Data Only”, Penedo et al 2023
“Let’s Verify Step by Step”, Lightman et al 2023
“WikiChat: Stopping the Hallucination of Large Language Model Chatbots by Few-Shot Grounding on Wikipedia”, Semnani et al 2023
“SeeGULL: A Stereotype Benchmark With Broad Geo-Cultural Coverage Leveraging Generative Models”, Jha et al 2023
SeeGULL: A Stereotype Benchmark with Broad Geo-Cultural Coverage Leveraging Generative Models
“C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models”, Huang et al 2023
C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models
“TinyStories: How Small Can Language Models Be and Still Speak Coherent English?”, Eldan & Li 2023
TinyStories: How Small Can Language Models Be and Still Speak Coherent English?
“Pick-A-Pic: An Open Dataset of User Preferences for Text-To-Image Generation”, Kirstain et al 2023
Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation
“LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions”, Wu et al 2023
LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions
“Multi-Party Chat (MultiLIGHT): Conversational Agents in Group Settings With Humans and Models”, Wei et al 2023
Multi-Party Chat (MultiLIGHT): Conversational Agents in Group Settings with Humans and Models
“ImageNet-Hard: The Hardest Images Remaining from a Study of the Power of Zoom and Spatial Biases in Image Classification”, Taesiri et al 2023
“Parsing-Conditioned Anime Translation: A New Dataset and Method”, Li et al 2023c
Parsing-Conditioned Anime Translation: A New Dataset and Method
“Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling”, Biderman et al 2023
Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling
“Abstraction-Perception Preserving Cartoon Face Synthesis”, Ho et al 2023
“How Well Do Large Language Models Perform in Arithmetic Tasks?”, Yuan et al 2023
How well do Large Language Models perform in Arithmetic tasks?
“The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset”, Laurençon et al 2023
The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset
“Large Language Models Are State-Of-The-Art Evaluators of Translation Quality”, Kocmi & Federmann 2023
Large Language Models Are State-of-the-Art Evaluators of Translation Quality
“Benchmarks for Automated Commonsense Reasoning: A Survey”, Davis 2023
“Off-The-Grid MARL (OG-MARL): Datasets With Baselines for Offline Multi-Agent Reinforcement Learning”, Formanek et al 2023
Off-the-Grid MARL (OG-MARL): Datasets with Baselines for Offline Multi-Agent Reinforcement Learning
“The BabyLM Challenge: Sample-Efficient Pretraining on a Developmentally Plausible Corpus”, Warstadt et al 2023
The BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus
“The Semantic Scholar Open Data Platform”, Kinney et al 2023
“Interactive-Chain-Prompting (INTERCPT): Ambiguity Resolution for Crosslingual Conditional Generation With Interaction”, Pilault et al 2023
“How Close Is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection”, Guo et al 2023
How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection
“Med-PaLM: Large Language Models Encode Clinical Knowledge”, Singhal et al 2022
“Unnatural Instructions: Tuning Language Models With (Almost) No Human Labor”, Honovich et al 2022
Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor
“HALIE: Evaluating Human-Language Model Interaction”, Lee et al 2022
“A Whack-A-Mole Dilemma: Shortcuts Come in Multiples Where Mitigating One Amplifies Others”, Li et al 2022
A Whack-A-Mole Dilemma: Shortcuts Come in Multiples Where Mitigating One Amplifies Others
“Text Embeddings by Weakly-Supervised Contrastive Pre-Training”, Wang et al 2022
Text Embeddings by Weakly-Supervised Contrastive Pre-training
“The Stack: 3 TB of Permissively Licensed Source Code”, Kocetkov et al 2022
“UniSumm: Unified Few-Shot Summarization With Multi-Task Pre-Training and Prefix-Tuning”, Chen et al 2022
UniSumm: Unified Few-shot Summarization with Multi-Task Pre-Training and Prefix-Tuning
“A Creative Industry Image Generation Dataset Based on Captions”, Yuejia et al 2022
A Creative Industry Image Generation Dataset Based on Captions
“AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities”, Chen et al 2022
AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities
“AnimeRun: 2D Animation Visual Correspondence from Open Source 3D Movies”, Siyao et al 2022
AnimeRun: 2D Animation Visual Correspondence from Open Source 3D Movies
“MMDialog: A Large-Scale Multi-Turn Dialogue Dataset Towards Multi-Modal Open-Domain Conversation”, Feng et al 2022
MMDialog: A Large-scale Multi-turn Dialogue Dataset Towards Multi-modal Open-domain Conversation
“BLOOMZ/mT0: Crosslingual Generalization through Multitask Finetuning”, Muennighoff et al 2022
BLOOMZ/mT0: Crosslingual Generalization through Multitask Finetuning
“Dungeons and Data: A Large-Scale NetHack Dataset”, Hambro et al 2022
“Will We Run out of Data? An Analysis of the Limits of Scaling Datasets in Machine Learning”, Villalobos et al 2022
Will we run out of data? An analysis of the limits of scaling datasets in Machine Learning
“Large Language Models Can Self-Improve”, Huang et al 2022
“CARP: Robust Preference Learning for Storytelling via Contrastive Reinforcement Learning”, Castricato et al 2022
CARP: Robust Preference Learning for Storytelling via Contrastive Reinforcement Learning
“MTEB: Massive Text Embedding Benchmark”, Muennighoff et al 2022
“Most Language Models Can Be Poets Too: An AI Writing Assistant and Constrained Text Generation Studio”, Roush et al 2022
“Self-Ask: Measuring and Narrowing the Compositionality Gap in Language Models (Bamboogle)”, Press et al 2022
Self-Ask: Measuring and Narrowing the Compositionality Gap in Language Models (Bamboogle)
“Dynamic Prompt Learning via Policy Gradient for Semi-Structured Mathematical Reasoning”, Lu et al 2022
Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning
“Brain Imaging Generation With Latent Diffusion Models”, Pinaya et al 2022
“PaLI: A Jointly-Scaled Multilingual Language-Image Model”, Chen et al 2022
“FOLIO: Natural Language Reasoning With First-Order Logic”, Han et al 2022
“Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned”, Ganguli et al 2022
Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned
“Bugs in the Data: How ImageNet Misrepresents Biodiversity”, Luccioni & Rolnick 2022
“Discovering Bugs in Vision Models Using Off-The-Shelf Image Generation and Captioning”, Wiles et al 2022
Discovering Bugs in Vision Models using Off-the-shelf Image Generation and Captioning
“Benchmarking Compositionality With Formal Languages”, Valvoda et al 2022
“Quality Not Quantity: On the Interaction between Dataset Design and Robustness of CLIP”, Nguyen et al 2022
Quality Not Quantity: On the Interaction between Dataset Design and Robustness of CLIP
“Learning to Generalize With Object-Centric Agents in the Open World Survival Game Crafter”, Stanić et al 2022
Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter
“Few-Shot Adaptation Works With UnpredicTable Data”, Chan et al 2022
“Language Models Can Teach Themselves to Program Better”, Haluptzok et al 2022
“RealTime QA: What’s the Answer Right Now?”, Kasai et al 2022
“NewsStories: Illustrating Articles With Visual Summaries”, Tan et al 2022
“CelebV-HQ: A Large-Scale Video Facial Attributes Dataset”, Zhu et al 2022
“Why Do Tree-Based Models Still Outperform Deep Learning on Tabular Data?”, Grinsztajn et al 2022
Why do tree-based models still outperform deep learning on tabular data?
“Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset”, Henderson et al 2022
Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset
“Forecasting Future World Events With Neural Networks”, Zou et al 2022
“RST: ReStructured Pre-Training”, Yuan & Liu 2022
“Learning to Generate Artistic Character Line Drawing”, Fang et al 2022
“Dataset Condensation via Efficient Synthetic-Data Parameterization”, Kim et al 2022
Dataset Condensation via Efficient Synthetic-Data Parameterization
“Bongard-HOI: Benchmarking Few-Shot Visual Reasoning for Human-Object Interactions”, Jiang et al 2022
Bongard-HOI: Benchmarking Few-Shot Visual Reasoning for Human-Object Interactions
“Fine-Grained Image Captioning With CLIP Reward”, Cho et al 2022
“FLEURS: Few-Shot Learning Evaluation of Universal Representations of Speech”, Conneau et al 2022
FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech
“InstructDial: Improving Zero and Few-Shot Generalization in Dialogue through Instruction Tuning”, Gupta et al 2022
InstructDial: Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuning
“Learning to Model Editing Processes”, Reid & Neubig 2022
“Flexible Diffusion Modeling of Long Videos”, Harvey et al 2022
“Housekeep: Tidying Virtual Households Using Commonsense Reasoning”, Kant et al 2022
Housekeep: Tidying Virtual Households using Commonsense Reasoning
“Instruction Induction: From Few Examples to Natural Language Task Descriptions”, Honovich et al 2022
Instruction Induction: From Few Examples to Natural Language Task Descriptions
“Down and Across: Introducing Crossword-Solving As a New NLP Benchmark”, Kulshreshtha et al 2022
Down and Across: Introducing Crossword-Solving as a New NLP Benchmark
“Automated Crossword Solving”, Wallace et al 2022
“Dialog Inpainting: Turning Documents into Dialogues”, Dai et al 2022
“SymphonyNet: Symphony Generation With Permutation Invariant Language Model”, Liu et al 2022
SymphonyNet: Symphony Generation with Permutation Invariant Language Model
“Building Machine Translation Systems for the Next Thousand Languages”, Bapna et al 2022
Building Machine Translation Systems for the Next Thousand Languages
“When Does Dough Become a Bagel? Analyzing the Remaining Mistakes on ImageNet”, Vasudevan et al 2022
When does dough become a bagel? Analyzing the remaining mistakes on ImageNet
“Data Determines Distributional Robustness in Contrastive Language Image Pre-Training (CLIP)”, Fang et al 2022
Data Determines Distributional Robustness in Contrastive Language Image Pre-training (CLIP)
“A Challenging Benchmark of Anime Style Recognition”, Li et al 2022
“Tk-Instruct: Benchmarking Generalization via In-Context Instructions on 1,600+ Language Tasks”, Wang et al 2022
Tk-Instruct: Benchmarking Generalization via In-Context Instructions on 1,600+ Language Tasks
“Winoground: Probing Vision and Language Models for Visio-Linguistic Compositionality”, Thrush et al 2022
Winoground: Probing Vision and Language Models for Visio-Linguistic Compositionality
“KNN-Diffusion: Image Generation via Large-Scale Retrieval”, Ashual et al 2022
“ByT5 Model for Massively Multilingual Grapheme-To-Phoneme Conversion”, Zhu et al 2022
ByT5 model for massively multilingual grapheme-to-phoneme conversion
“STaR: Bootstrapping Reasoning With Reasoning”, Zelikman et al 2022
“CLIP Meets GamePhysics: Towards Bug Identification in Gameplay Videos Using Zero-Shot Transfer Learning”, Taesiri et al 2022
“Bamboo: Building Mega-Scale Vision Dataset Continually With Human-Machine Synergy”, Zhang et al 2022
Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine Synergy
“Self-Distilled StyleGAN: Towards Generation from Internet Photos”, Mokady et al 2022
Self-Distilled StyleGAN: Towards Generation from Internet Photos
“RuCLIP—New Models and Experiments: a Technical Report”, Shonenkov et al 2022
“Wukong: 100 Million Large-Scale Chinese Cross-Modal Pre-Training Dataset and A Foundation Framework”, Gu et al 2022
Wukong: 100 Million Large-scale Chinese Cross-modal Pre-training Dataset and A Foundation Framework
“ROME: Locating and Editing Factual Associations in GPT”, Meng et al 2022
“DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-To-Image Generative Transformers”, Cho et al 2022
DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generative Transformers
“PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts”, Bach et al 2022
PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts
“StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets”, Sauer et al 2022
“BLIP: Bootstrapping Language-Image Pre-Training for Unified Vision-Language Understanding and Generation”, Li et al 2022
“Can Wikipedia Help Offline Reinforcement Learning?”, Reid et al 2022
“SWAG: Revisiting Weakly Supervised Pre-Training of Visual Perception Models”, Singh et al 2022
SWAG: Revisiting Weakly Supervised Pre-Training of Visual Perception Models
“CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities”, Lee et al 2022
“WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation”, Liu et al 2022
WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation
“SynthBio: A Case Study in Faster Curation of Text Datasets”, Yuan et al 2022
“BigDatasetGAN: Synthesizing ImageNet With Pixel-Wise Annotations”, Li et al 2022
BigDatasetGAN: Synthesizing ImageNet with Pixel-wise Annotations
“ERNIE-ViLG: Unified Generative Pre-Training for Bidirectional Vision-Language Generation”, Zhang et al 2021
ERNIE-ViLG: Unified Generative Pre-training for Bidirectional Vision-Language Generation
“A Fistful of Words: Learning Transferable Visual Models from Bag-Of-Words Supervision”, Tejankar et al 2021
A Fistful of Words: Learning Transferable Visual Models from Bag-of-Words Supervision
“GLIDE: Towards Photorealistic Image Generation and Editing With Text-Guided Diffusion Models”, Nichol et al 2021
GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
“FRUIT: Faithfully Reflecting Updated Information in Text”, IV et al 2021
“Models in the Loop: Aiding Crowdworkers With Generative Annotation Assistants”, Bartolo et al 2021
Models in the Loop: Aiding Crowdworkers with Generative Annotation Assistants
“WebGPT: Browser-Assisted Question-Answering With Human Feedback”, Nakano et al 2021
WebGPT: Browser-assisted question-answering with human feedback
“GLaM: Efficient Scaling of Language Models With Mixture-Of-Experts”, Du et al 2021
GLaM: Efficient Scaling of Language Models with Mixture-of-Experts
“MAD: A Scalable Dataset for Language Grounding in Videos from Movie Audio Descriptions”, Soldan et al 2021
MAD: A Scalable Dataset for Language Grounding in Videos from Movie Audio Descriptions
“BASIC: Combined Scaling for Open-Vocabulary Image Classification”, Pham et al 2021
BASIC: Combined Scaling for Open-Vocabulary Image Classification
“Solving Probability and Statistics Problems by Program Synthesis”, Tang et al 2021
Solving Probability and Statistics Problems by Program Synthesis
“Few-Shot Self-Rationalization With Natural Language Prompts”, Marasović et al 2021
“AnimeCeleb: Large-Scale Animation CelebHeads Dataset for Head Reenactment”, Kim et al 2021
AnimeCeleb: Large-Scale Animation CelebHeads Dataset for Head Reenactment
“RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement Learning”, Ramos et al 2021
RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement Learning
“An Explanation of In-Context Learning As Implicit Bayesian Inference”, Xie et al 2021
An Explanation of In-context Learning as Implicit Bayesian Inference
“LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs”, Schuhmann et al 2021
LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs
“Training Verifiers to Solve Math Word Problems”, Cobbe et al 2021
“A Connectome of the Drosophila Central Complex Reveals Network Motifs Suitable for Flexible Navigation and Context-Dependent Action Selection”, Hulse et al 2021
“HTCN: Harmonious Text Colorization Network for Visual-Textual Presentation Design”, Yang et al 2021c
HTCN: Harmonious Text Colorization Network for Visual-Textual Presentation Design
“T0: Multitask Prompted Training Enables Zero-Shot Task Generalization”, Sanh et al 2021
T0: Multitask Prompted Training Enables Zero-Shot Task Generalization
“Can Machines Learn Morality? The Delphi Experiment”, Jiang et al 2021
“Situated Dialogue Learning through Procedural Environment Generation”, Ammanabrolu et al 2021
Situated Dialogue Learning through Procedural Environment Generation
“MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research”, Samvelyan et al 2021
MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research
“TruthfulQA: Measuring How Models Mimic Human Falsehoods”, Lin et al 2021
“MiniF2F: a Cross-System Benchmark for Formal Olympiad-Level Mathematics”, Zheng et al 2021
MiniF2F: a cross-system benchmark for formal Olympiad-level mathematics
“LAION-400-Million Open Dataset”, Schuhmann 2021
“Transfer Learning for Pose Estimation of Illustrated Characters”, Chen & Zwicker 2021
Transfer Learning for Pose Estimation of Illustrated Characters
“MuSiQue: Multi-Hop Questions via Single-Hop Question Composition”, Trivedi et al 2021
MuSiQue: Multi-hop Questions via Single-hop Question Composition
“Scaling Vision Transformers”, Zhai et al 2021
“XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyond”, Barbieri et al 2021
XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyond
“BEIR: A Heterogenous Benchmark for Zero-Shot Evaluation of Information Retrieval Models”, Thakur et al 2021
BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models
“SpeechStew: Simply Mix All Available Speech Recognition Data to Train One Large Neural Network”, Chan et al 2021
SpeechStew: Simply Mix All Available Speech Recognition Data to Train One Large Neural Network
“Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks”, Northcutt et al 2021
Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks
“NaturalProofs: Mathematical Theorem Proving in Natural Language”, Welleck et al 2021
NaturalProofs: Mathematical Theorem Proving in Natural Language
“Get Your Vitamin C! Robust Fact Verification With Contrastive Evidence (VitaminC)”, Schuster et al 2021
Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence (VitaminC)
“Are NLP Models Really Able to Solve Simple Math Word Problems?”, Patel et al 2021
Are NLP Models really able to Solve Simple Math Word Problems?
“Measuring Mathematical Problem Solving With the MATH Dataset”, Hendrycks et al 2021
Measuring Mathematical Problem Solving With the MATH Dataset
“WIT: Wikipedia-Based Image Text Dataset for Multimodal Multilingual Machine Learning”, Srinivasan et al 2021
WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning
“A Massive 7T FMRI Dataset to Bridge Cognitive and Computational Neuroscience”, Allen et al 2021
A massive 7T fMRI dataset to bridge cognitive and computational neuroscience
“Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts”, Changpinyo et al 2021
Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts
“ALIGN: Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision”, Jia et al 2021
ALIGN: Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision
“Mind the Gap: Assessing Temporal Generalization in Neural Language Models § Scaling”, Lazaridou et al 2021
Mind the Gap: Assessing Temporal Generalization in Neural Language Models § Scaling
“Scaling Laws for Transfer”, Hernandez et al 2021
“Automatic Curation of Large-Scale Datasets for Audio-Visual Representation Learning”, Lee et al 2021
Automatic Curation of Large-Scale Datasets for Audio-Visual Representation Learning
“MSR-VTT: A Large Video Description Dataset for Bridging Video and Language”, Xu et al 2021
MSR-VTT: A Large Video Description Dataset for Bridging Video and Language
“CLIP: Learning Transferable Visual Models From Natural Language Supervision”, Radford et al 2021
CLIP: Learning Transferable Visual Models From Natural Language Supervision
“CLIP: Connecting Text and Images: We’re Introducing a Neural Network Called CLIP Which Efficiently Learns Visual Concepts from Natural Language Supervision. CLIP Can Be Applied to Any Visual Classification Benchmark by Simply Providing the Names of the Visual Categories to Be Recognized, Similar to the ‘Zero-Shot’ Capabilities of GPT-2 and GPT-3”, Radford et al 2021
“The Pile: An 800GB Dataset of Diverse Text for Language Modeling”, Gao et al 2021
The Pile: An 800GB Dataset of Diverse Text for Language Modeling
“Selective Eye-Gaze Augmentation To Enhance Imitation Learning In Atari Games”, Thammineni et al 2020
Selective Eye-gaze Augmentation To Enhance Imitation Learning In Atari Games
“VoxLingua107: a Dataset for Spoken Language Recognition”, Valk & Alumäe 2020
“MoGaze: A Dataset of Full-Body Motions That Includes Workspace Geometry and Eye-Gaze”, Kratzer et al 2020
MoGaze: A Dataset of Full-Body Motions that Includes Workspace Geometry and Eye-Gaze
“End-To-End Chinese Landscape Painting Creation Using Generative Adversarial Networks”, Xue 2020
End-to-End Chinese Landscape Painting Creation Using Generative Adversarial Networks
“Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding”, Roberts et al 2020
Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding
“Constructing A Multi-Hop QA Dataset for Comprehensive Evaluation of Reasoning Steps”, Ho et al 2020
Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps
“Language ID in the Wild: Unexpected Challenges on the Path to a Thousand-Language Web Text Corpus”, Caswell et al 2020
Language ID in the Wild: Unexpected Challenges on the Path to a Thousand-Language Web Text Corpus
“Open-Domain Question Answering Goes Conversational via Question Rewriting”, Anantha et al 2020
Open-Domain Question Answering Goes Conversational via Question Rewriting
“Digital Voicing of Silent Speech”, Gaddy & Klein 2020
“A C/C++ Code Vulnerability Dataset With Code Changes and CVE Summaries”, Fan et al 2020
A C/C++ Code Vulnerability Dataset with Code Changes and CVE Summaries
“MMLU: Measuring Massive Multitask Language Understanding”, Hendrycks et al 2020
“Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing”, Gu et al 2020
Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing
“CoVoST 2 and Massively Multilingual Speech-To-Text Translation”, Wang et al 2020
CoVoST 2 and Massively Multilingual Speech-to-Text Translation
“The Many Faces of Robustness: A Critical Analysis of Out-Of-Distribution Generalization”, Hendrycks et al 2020
The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization
“The NetHack Learning Environment”, Küttler et al 2020
“Anime Crop Datasets: Faces, Figures, & Hands”, Gwern et al 2020
“ForecastQA: A Question Answering Challenge for Event Forecasting With Temporal Text Data”, Jin et al 2020
ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Data
“Shortcut Learning in Deep Neural Networks”, Geirhos et al 2020
“D4RL: Datasets for Deep Data-Driven Reinforcement Learning”, Fu et al 2020
“TyDiQA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages”, Clark et al 2020
TyDiQA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages
“SAYCam: A Large, Longitudinal Audiovisual Dataset Recorded from the Infant’s Perspective”, Sullivan et al 2020
SAYCam: A large, longitudinal audiovisual dataset recorded from the infant’s perspective
“ImageNet-A: Natural Adversarial Examples”, Hendrycks et al 2020
“Measuring Compositional Generalization: A Comprehensive Method on Realistic Data”, Keysers et al 2019
Measuring Compositional Generalization: A Comprehensive Method on Realistic Data
“Libri-Light: A Benchmark for ASR With Limited or No Supervision”, Kahn et al 2019
Libri-Light: A Benchmark for ASR with Limited or No Supervision
“How Can We Know What Language Models Know?”, Jiang et al 2019
“SimpleBooks: Long-Term Dependency Book Dataset With Simplified English Vocabulary for Word-Level Language Modeling”, Nguyen 2019
“How Machine Learning Can Help Unlock the World of Ancient Japan”, Lamb 2019
How Machine Learning Can Help Unlock the World of Ancient Japan
“Compressive Transformers for Long-Range Sequence Modelling”, Rae et al 2019
“CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning”, Lin et al 2019
CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning
“CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data”, Wenzek et al 2019
CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data
“T5: Exploring the Limits of Transfer Learning With a Unified Text-To-Text Transformer”, Raffel et al 2019
T5: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
“Restoring Ancient Text Using Deep Learning (Pythia): a Case Study on Greek Epigraphy”, Assael et al 2019
Restoring ancient text using deep learning (Pythia): a case study on Greek epigraphy
“CATER: A Diagnostic Dataset for Compositional Actions and TEmporal Reasoning”, Girdhar & Ramanan 2019
CATER: A diagnostic dataset for Compositional Actions and TEmporal Reasoning
“PubMedQA: A Dataset for Biomedical Research Question Answering”, Jin et al 2019
PubMedQA: A Dataset for Biomedical Research Question Answering
“ObjectNet: A Large-Scale Bias-Controlled Dataset for Pushing the Limits of Object Recognition Models”, Barbu et al 2019
ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models
“No Press Diplomacy: Modeling Multi-Agent Gameplay”, Paquette et al 2019
“LVIS: A Dataset for Large Vocabulary Instance Segmentation”, Gupta et al 2019
“Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank”, Socher et al 2019
Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
“A Large Single-Participant FMRI Dataset for Probing Brain Responses to Naturalistic Stimuli in Space and Time”, Seeliger et al 2019
“OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge”, Marino et al 2019
OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge
“ImageNet-Sketch: Learning Robust Global Representations by Penalizing Local Predictive Power”, Wang et al 2019
ImageNet-Sketch: Learning Robust Global Representations by Penalizing Local Predictive Power
“SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems”, Wang et al 2019
SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems
“The MineRL 2019 Competition on Sample Efficient Reinforcement Learning Using Human Priors”, Guss et al 2019
The MineRL 2019 Competition on Sample Efficient Reinforcement Learning using Human Priors
“ProductNet: a Collection of High-Quality Datasets for Product Representation Learning”, Wang et al 2019
ProductNet: a Collection of High-Quality Datasets for Product Representation Learning
“Benchmarking Neural Network Robustness to Common Corruptions and Perturbations”, Hendrycks & Dietterich 2019
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
“Atari-HEAD: Atari Human Eye-Tracking and Demonstration Dataset”, Zhang et al 2019
Atari-HEAD: Atari Human Eye-Tracking and Demonstration Dataset
“LIGHT: Learning to Speak and Act in a Fantasy Text Adventure Game”, Urbanek et al 2019
LIGHT: Learning to Speak and Act in a Fantasy Text Adventure Game
“A Replication Study: Machine Learning Models Are Capable of Predicting Sexual Orientation From Facial Images”, Leuner 2019
“Language Models Are Unsupervised Multitask Learners”, Radford et al 2019
“The Omniglot Challenge: a 3-Year Progress Report”, Lake et al 2019
“Do We Train on Test Data? Purging CIFAR of Near-Duplicates”, Barz & Denzler 2019
“The RobotriX: An EXtremely Photorealistic and Very-Large-Scale Indoor Dataset of Sequences With Robot Trajectories and Interactions”, Garcia-Garcia et al 2019
“FIGR: Few-Shot Image Generation With Reptile”, Clouâtre & Demers 2019
“Natural Questions: A Benchmark for Question Answering Research”, Kwiatkowski et al 2019
Natural Questions: A Benchmark for Question Answering Research
“A Style-Based Generator Architecture for Generative Adversarial Networks”, Karras et al 2018
A Style-Based Generator Architecture for Generative Adversarial Networks
“ImageNet-Trained CNNs Are Biased towards Texture; Increasing Shape Bias Improves Accuracy and Robustness”, Geirhos et al 2018
“CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge”, Talmor et al 2018
CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge
“The Open Images Dataset V4: Unified Image Classification, Object Detection, and Visual Relationship Detection at Scale”, Kuznetsova et al 2018
“HotpotQA: A Dataset for Diverse, Explainable Multi-Hop Question Answering”, Yang et al 2018
HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
“Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization”, Narayan et al 2018
“CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images”, Guo et al 2018
CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images
“A Short Note about Kinetics-600”, Carreira et al 2018
“Cartoon Set”, Royer et al 2018
“Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations”, Hendrycks & Dietterich 2018
Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations
“Know What You Don’t Know: Unanswerable Questions for SQuAD”, Rajpurkar et al 2018
“BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning”, Yu et al 2018
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
“Exploring the Limits of Weakly Supervised Pretraining”, Mahajan et al 2018
“Newsroom: A Dataset of 1.3 Million Summaries With Diverse Extractive Strategies”, Grusky et al 2018
Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies
“GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding”, Wang et al 2018
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
“The Sound of Pixels”, Zhao et al 2018
“FEVER: a Large-Scale Dataset for Fact Extraction and VERification”, Thorne et al 2018
FEVER: a large-scale dataset for Fact Extraction and VERification
“Think You Have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge”, Clark et al 2018
Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
“SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction”, Liang et al 2018
SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction
“11K Hands: Gender Recognition and Biometric Identification Using a Large Dataset of Hand Images”, Afifi 2017
11K Hands: Gender recognition and biometric identification using a large dataset of hand images
“OpenML Benchmarking Suites”, Bischl et al 2017
“WebVision Database: Visual Learning and Understanding from Web Data”, Li et al 2017
WebVision Database: Visual Learning and Understanding from Web Data
“A Downsampled Variant of ImageNet As an Alternative to the CIFAR Datasets”, Chrabaszcz et al 2017
A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets
“Revisiting Unreasonable Effectiveness of Data in Deep Learning Era”, Sun et al 2017
Revisiting Unreasonable Effectiveness of Data in Deep Learning Era
“Driver Identification Using Automobile Sensor Data from a Single Turn”, Hallac et al 2017
Driver Identification Using Automobile Sensor Data from a Single Turn
“StreetStyle: Exploring World-Wide Clothing Styles from Millions of Photos”, Matzen et al 2017
StreetStyle: Exploring world-wide clothing styles from millions of photos
“The Kinetics Human Action Video Dataset”, Kay et al 2017
“WebVision Challenge: Visual Learning and Understanding With Web Data”, Li et al 2017
WebVision Challenge: Visual Learning and Understanding With Web Data
“TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension”, Joshi et al 2017
TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
“Dense-Captioning Events in Videos”, Krishna et al 2017
“BAM! The Behance Artistic Media Dataset for Recognition Beyond Photography”, Wilber et al 2017
BAM! The Behance Artistic Media Dataset for Recognition Beyond Photography
“SearchQA: A New Q&A Dataset Augmented With Context from a Search Engine”, Dunn et al 2017
SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine
“RACE: Large-Scale ReAding Comprehension Dataset From Examinations”, Lai et al 2017
RACE: Large-scale ReAding Comprehension Dataset From Examinations
“NewsQA: A Machine Comprehension Dataset”, Trischler et al 2016
“MS MARCO: A Human Generated MAchine Reading COmprehension Dataset”, Bajaj et al 2016
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
“Lip Reading Sentences in the Wild”, Chung et al 2016
“Pointer Sentinel Mixture Models”, Merity et al 2016
“Solving General Arithmetic Word Problems”, Roy & Roth 2016
“The LAMBADA Dataset: Word Prediction Requiring a Broad Discourse Context”, Paperno et al 2016
The LAMBADA dataset: Word prediction requiring a broad discourse context
“SQuAD: 100,000+ Questions for Machine Comprehension of Text”, Rajpurkar et al 2016
“Convolutional Sketch Inversion”, Güçlütürk et al 2016
“The MovieLens Datasets: History and Context”, Harper & Konstan 2015
“Neural Module Networks”, Andreas et al 2015
“Sketch-Based Manga Retrieval Using Manga109 Dataset”, Matsui et al 2015
“Amazon Reviews: Image-Based Recommendations on Styles and Substitutes”, McAuley et al 2015
Amazon Reviews: Image-based Recommendations on Styles and Substitutes
“Teaching Machines to Read and Comprehend”, Hermann et al 2015
“LSUN: Construction of a Large-Scale Image Dataset Using Deep Learning With Humans in the Loop”, Yu et al 2015
LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop
“VQA: Visual Question Answering”, Agrawal et al 2015
“YFCC100M: The New Data in Multimedia Research”, Thomee et al 2015
“ImageNet Large Scale Visual Recognition Challenge”, Russakovsky et al 2014
“Microsoft COCO: Common Objects in Context”, Lin et al 2014
“N-Gram Counts and Language Models from the Common Crawl”, Buck et al 2014
“Ukiyo-E Search”, Resig 2013
“UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild”, Soomro et al 2012
UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
“The Caltech-UCSD Birds-200-2011 Dataset”, Wah et al 2011
“Unbiased Look at Dataset Bias”, Torralba & Efros 2011
“Caltech-UCSD Birds 200”, Welinder et al 2010
“Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments”, Huang et al 2008
Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments
“Building a Large Annotated Corpus of English: The Penn Treebank”, Marcus et al 1993
Building a Large Annotated Corpus of English: The Penn Treebank
“Solving Math Word Problems: We’ve Trained a System That Solves Grade School Math Problems With Nearly Twice the Accuracy of a Fine-Tuned GPT-3 Model. It Solves about 90% As Many Problems As Real Kids: a Small Sample of 9-12 Year Olds Scored 60% on a Test from Our Dataset, While Our System Scored 55% on Those Same Problems. This Is Important Because Today’s AI Is Still Quite Weak at Commonsense Multistep Reasoning, Which Is Easy Even for Grade School Kids. We Achieved These Results by Training Our Model to Recognize Its Mistakes, so That It Can Try Repeatedly Until It Finds a Solution That Works”
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http://cl-informatik.uibk.ac.at/cek/holstep/ckfccs-holstep-submitted.pdf -
https://karpathy.github.io/2011/04/27/manually-classifying-cifar10/:View External Link:
https://karpathy.github.io/2011/04/27/manually-classifying-cifar10/ -
https://openaccess.thecvf.com/content_cvpr_2014/papers/Andriluka_2D_Human_Pose_2014_CVPR_paper.pdf -
https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/37648.pdf -
https://twitter.com/felix_red_panda/status/1723324786808692887:
Link Bibliography
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https://www.nytimes.com/2024/04/06/technology/tech-giants-harvest-data-artificial-intelligence.html: “How Tech Giants Cut Corners to Harvest Data for AI: OpenAI, Google and Meta Ignored Corporate Policies, Altered Their Own Rules and Discussed Skirting Copyright Law As They Sought Online Information to Train Their Newest Artificial Intelligence Systems”, Cade Metz, Cecilia Kang, Sheera Frenkel, Stuart A. Thompson, Nico Grant -
https://arxiv.org/abs/2403.18624: “Vulnerability Detection With Code Language Models: How Far Are We?”, -
https://arxiv.org/abs/2312.11556: “StarVector: Generating Scalable Vector Graphics Code from Images”, Juan A. Rodriguez, Shubham Agarwal, Issam H. Laradji, Pau Rodriguez, David Vazquez, Christopher Pal, Marco Pedersoli -
https://arxiv.org/abs/2311.13657: “Efficient Transformer Knowledge Distillation: A Performance Review”, Nathan Brown, Ashton Williamson, Tahj Anderson, Logan Lawrence -
https://arxiv.org/abs/2310.16825: “CommonCanvas: An Open Diffusion Model Trained With Creative-Commons Images”, -
https://arxiv.org/abs/2310.06786: “OpenWebMath: An Open Dataset of High-Quality Mathematical Web Text”, Keiran Paster, Marco Dos Santos, Zhangir Azerbayev, Jimmy Ba -
https://arxiv.org/abs/2310.03214#google: “FreshLLMs: Refreshing Large Language Models With Search Engine Augmentation”, -
https://arxiv.org/abs/2310.01377: “UltraFeedback: Boosting Language Models With High-Quality Feedback”, Ganqu Cui, Lifan Yuan, Ning Ding, Guanming Yao, Wei Zhu, Yuan Ni, Guotong Xie, Zhiyuan Liu, Maosong Sun -
https://arxiv.org/abs/2309.16671: “Demystifying CLIP Data”, -
https://arxiv.org/abs/2309.12269: “The Cambridge Law Corpus: A Corpus for Legal AI Research”, -
https://arxiv.org/abs/2309.10818#cerebras: “SlimPajama-DC: Understanding Data Combinations for LLM Training”, -
https://arxiv.org/abs/2309.04269: “From Sparse to Dense: GPT-4 Summarization With Chain of Density (CoD) Prompting”, Griffin Adams, Alexander Fabbri, Faisal Ladhak, Eric Lehman, Noémie Elhadad -
https://arxiv.org/abs/2308.12477: “American Stories: A Large-Scale Structured Text Dataset of Historical U.S. Newspapers”, -
https://arxiv.org/abs/2307.08701#samsung: “AlpaGasus: Training A Better Alpaca With Fewer Data”, -
https://arxiv.org/abs/2307.05014: “Test-Time Training on Video Streams”, Renhao Wang, Yu Sun, Yossi Gandelsman, Xinlei Chen, Alexei A. Efros, Xiaolong Wang -
https://arxiv.org/abs/2306.15626: “LeanDojo: Theorem Proving With Retrieval-Augmented Language Models”, -
https://arxiv.org/abs/2306.12587: “ARIES: A Corpus of Scientific Paper Edits Made in Response to Peer Reviews”, Mike D’Arcy, Alexis Ross, Erin Bransom, Bailey Kuehl, Jonathan Bragg, Tom Hope, Doug Downey -
https://www.theverge.com/features/23764584/ai-artificial-intelligence-data-notation-labor-scale-surge-remotasks-openai-chatbots: “AI Is a Lot of Work: As the Technology Becomes Ubiquitous, a Vast Tasker Underclass Is Emerging—And Not Going Anywhere”, Josh Dzieza -
2023-yi.pdf: “Anime Character Identification and Tag Prediction by Multimodality Modeling: Dataset and Model”, Fan Yi, Jiaxiang Wu, Minyi Zhao, Shuigeng Zhou -
https://www.theinformation.com/articles/why-youtube-could-give-google-an-edge-in-ai: “Why YouTube Could Give Google an Edge in AI”, Jon Victor -
https://arxiv.org/abs/2305.20050#openai: “Let’s Verify Step by Step”, -
https://arxiv.org/abs/2305.07759#microsoft: “TinyStories: How Small Can Language Models Be and Still Speak Coherent English?”, Ronen Eldan, Yuanzhi Li -
https://arxiv.org/abs/2305.01569: “Pick-A-Pic: An Open Dataset of User Preferences for Text-To-Image Generation”, Yuval Kirstain, Adam Polyak, Uriel Singer, Shahbuland Matiana, Joe Penna, Omer Levy -
https://arxiv.org/abs/2304.05538: “ImageNet-Hard: The Hardest Images Remaining from a Study of the Power of Zoom and Spatial Biases in Image Classification”, Mohammad Reza Taesiri, Giang Nguyen, Sarra Habchi, Cor-Paul Bezemer, Anh Nguyen -
https://arxiv.org/abs/2304.02015#alibaba: “How Well Do Large Language Models Perform in Arithmetic Tasks?”, Zheng Yuan, Hongyi Yuan, Chuanqi Tan, Wei Wang, Songfang Huang -
https://arxiv.org/abs/2302.14520: “Large Language Models Are State-Of-The-Art Evaluators of Translation Quality”, Tom Kocmi, Christian Federmann -
https://arxiv.org/abs/2212.13138#google: “Med-PaLM: Large Language Models Encode Clinical Knowledge”, -
https://arxiv.org/abs/2212.03533#microsoft: “Text Embeddings by Weakly-Supervised Contrastive Pre-Training”, Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei -
https://arxiv.org/abs/2211.15533: “The Stack: 3 TB of Permissively Licensed Source Code”, -
https://arxiv.org/abs/2211.06679#baai: “AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities”, Zhongzhi Chen, Guang Liu, Bo-Wen Zhang, Fulong Ye, Qinghong Yang, Ledell Wu -
https://arxiv.org/abs/2211.01786: “BLOOMZ/mT0: Crosslingual Generalization through Multitask Finetuning”, -
https://arxiv.org/abs/2210.11610#google: “Large Language Models Can Self-Improve”, Jiaxin Huang, Shixiang Shane Gu, Le Hou, Yuexin Wu, Xuezhi Wang, Hongkun Yu, Jiawei Han -
https://arxiv.org/abs/2210.07792#eleutherai: “CARP: Robust Preference Learning for Storytelling via Contrastive Reinforcement Learning”, -
https://aclanthology.org/2022.cai-1.2.pdf: “Most Language Models Can Be Poets Too: An AI Writing Assistant and Constrained Text Generation Studio”, Allen Roush, Sanjay Basu, Akshay Moorthy, Dmitry Dubovoy -
https://arxiv.org/abs/2210.03350#allen: “Self-Ask: Measuring and Narrowing the Compositionality Gap in Language Models (Bamboogle)”, -
https://arxiv.org/abs/2209.00840: “FOLIO: Natural Language Reasoning With First-Order Logic”, -
https://www.anthropic.com/red_teaming.pdf: “Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned”, -
https://arxiv.org/abs/2208.08831#deepmind: “Discovering Bugs in Vision Models Using Off-The-Shelf Image Generation and Captioning”, Olivia Wiles, Isabela Albuquerque, Sven Gowal -
https://arxiv.org/abs/2208.05516: “Quality Not Quantity: On the Interaction between Dataset Design and Robustness of CLIP”, Thao Nguyen, Gabriel Ilharco, Mitchell Wortsman, Sewoong Oh, Ludwig Schmidt -
https://arxiv.org/abs/2207.13061: “NewsStories: Illustrating Articles With Visual Summaries”, Reuben Tan, Bryan A. Plummer, Kate Saenko, J. P. Lewis, Avneesh Sud, Thomas Leung -
https://arxiv.org/abs/2206.15474: “Forecasting Future World Events With Neural Networks”, -
https://arxiv.org/abs/2205.09665#bair: “Automated Crossword Solving”, Eric Wallace, Nicholas Tomlin, Albert Xu, Kevin Yang, Eshaan Pathak, Matthew Ginsberg, Dan Klein -
https://arxiv.org/abs/2205.09073#google: “Dialog Inpainting: Turning Documents into Dialogues”, Zhuyun Dai, Arun Tejasvi Chaganty, Vincent Zhao, Aida Amini, Qazi Mamunur Rashid, Mike Green, Kelvin Guu -
https://arxiv.org/abs/2205.03983#google: “Building Machine Translation Systems for the Next Thousand Languages”, -
https://arxiv.org/abs/2205.04596#google: “When Does Dough Become a Bagel? Analyzing the Remaining Mistakes on ImageNet”, Vijay Vasudevan, Benjamin Caine, Raphael Gontijo-Lopes, Sara Fridovich-Keil, Rebecca Roelofs -
https://arxiv.org/abs/2205.01397: “Data Determines Distributional Robustness in Contrastive Language Image Pre-Training (CLIP)”, Alex Fang, Gabriel Ilharco, Mitchell Wortsman, Yuhao Wan, Vaishaal Shankar, Achal Dave, Ludwig Schmidt -
https://arxiv.org/abs/2204.07705: “Tk-Instruct: Benchmarking Generalization via In-Context Instructions on 1,600+ Language Tasks”, -
https://arxiv.org/abs/2204.03067: “ByT5 Model for Massively Multilingual Grapheme-To-Phoneme Conversion”, Jian Zhu, Cong Zhang, David Jurgens -
https://arxiv.org/abs/2203.11096: “CLIP Meets GamePhysics: Towards Bug Identification in Gameplay Videos Using Zero-Shot Transfer Learning”, Mohammad Reza Taesiri, Finlay Macklon, Cor-Paul Bezemer -
https://arxiv.org/abs/2202.12211#google: “Self-Distilled StyleGAN: Towards Generation from Internet Photos”, Ron Mokady, Michal Yarom, Omer Tov, Oran Lang, Daniel Cohen-Or, Tali Dekel, Michal Irani, Inbar Mosseri -
https://arxiv.org/abs/2202.06767#huawei: “Wukong: 100 Million Large-Scale Chinese Cross-Modal Pre-Training Dataset and A Foundation Framework”, -
https://arxiv.org/abs/2202.00273: “StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets”, Axel Sauer, Katja Schwarz, Andreas Geiger -
https://arxiv.org/abs/2201.12086#salesforce: “BLIP: Bootstrapping Language-Image Pre-Training for Unified Vision-Language Understanding and Generation”, Junnan Li, Dongxu Li, Caiming Xiong, Steven Hoi -
https://arxiv.org/abs/2201.08371#facebook: “SWAG: Revisiting Weakly Supervised Pre-Training of Visual Perception Models”, -
https://swabhs.com/assets/pdf/wanli.pdf#allen: “WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation”, Alisa Liu, Swabha Swayamdipta, <a href=https://nasmith.github.io/>N. A. Smith</a>, Yejin Choi -
https://arxiv.org/abs/2201.04684: “BigDatasetGAN: Synthesizing ImageNet With Pixel-Wise Annotations”, Daiqing Li, Huan Ling, Seung Wook Kim, Karsten Kreis, Adela Barriuso, Sanja Fidler, Antonio Torralba -
https://arxiv.org/abs/2112.15283#baidu: “ERNIE-ViLG: Unified Generative Pre-Training for Bidirectional Vision-Language Generation”, Han Zhang, Weichong Yin, Yewei Fang, Lanxin Li, Boqiang Duan, Zhihua Wu, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang -
https://arxiv.org/abs/2112.09332#openai: “WebGPT: Browser-Assisted Question-Answering With Human Feedback”, -
https://arxiv.org/abs/2111.10050#google: “BASIC: Combined Scaling for Open-Vocabulary Image Classification”, -
https://arxiv.org/abs/2111.08267: “Solving Probability and Statistics Problems by Program Synthesis”, Leonard Tang, Elizabeth Ke, Nikhil Singh, Nakul Verma, Iddo Drori -
https://arxiv.org/abs/2111.02114#laion: “LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs”, -
https://arxiv.org/abs/2110.14168#openai: “Training Verifiers to Solve Math Word Problems”, Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, John Schulman -
https://elifesciences.org/articles/66039: “A Connectome of the Drosophila Central Complex Reveals Network Motifs Suitable for Flexible Navigation and Context-Dependent Action Selection”, -
https://arxiv.org/abs/2109.07958: “TruthfulQA: Measuring How Models Mimic Human Falsehoods”, Stephanie Lin, Jacob Hilton, Owain Evans -
https://laion.ai/blog/laion-400-open-dataset/: “LAION-400-Million Open Dataset”, Christoph Schuhmann -
https://arxiv.org/abs/2106.04560#google: “Scaling Vision Transformers”, Xiaohua Zhai, Alexander Kolesnikov, Neil Houlsby, Lucas Beyer -
https://arxiv.org/abs/2103.14749: “Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks”, Curtis G. Northcutt, Anish Athalye, Jonas Mueller -
https://arxiv.org/abs/2102.05918#google: “ALIGN: Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision”, -
https://arxiv.org/abs/2102.01951#scaling&org=deepmind: “Mind the Gap: Assessing Temporal Generalization in Neural Language Models § Scaling”, -
https://cdn.openai.com/papers/Learning_Transferable_Visual_Models_From_Natural_Language_Supervision.pdf: “CLIP: Learning Transferable Visual Models From Natural Language Supervision”, -
https://openai.com/research/clip: “CLIP: Connecting Text and Images: We’re Introducing a Neural Network Called CLIP Which Efficiently Learns Visual Concepts from Natural Language Supervision. CLIP Can Be Applied to Any Visual Classification Benchmark by Simply Providing the Names of the Visual Categories to Be Recognized, Similar to the ‘Zero-Shot’ Capabilities of GPT-2 and GPT-3”, Alec Radford, Ilya Sutskever, Jong Wook Kim, Gretchen Krueger, Sandhini Agarwal -
https://arxiv.org/abs/2101.00027#eleutherai: “The Pile: An 800GB Dataset of Diverse Text for Language Modeling”, -
https://arxiv.org/abs/2010.14571#google: “Language ID in the Wild: Unexpected Challenges on the Path to a Thousand-Language Web Text Corpus”, Isaac Caswell, Theresa Breiner, Daan van Esch, Ankur Bapna -
https://arxiv.org/abs/2009.03300: “MMLU: Measuring Massive Multitask Language Understanding”, Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, Jacob Steinhardt -
crop: “Anime Crop Datasets: Faces, Figures, & Hands”, Gwern, Arfafax, Shawn Presser, Anonymous, Danbooru Community -
https://arxiv.org/abs/1911.05507#deepmind: “Compressive Transformers for Long-Range Sequence Modelling”, Jack W. Rae, Anna Potapenko, Siddhant M. Jayakumar, Timothy Lillicrap -
https://arxiv.org/abs/1905.00537: “SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems”, -
https://arxiv.org/abs/1808.01097: “CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images”, Sheng Guo, Weilin Huang, Haozhi Zhang, Chenfan Zhuang, Dengke Dong, Matthew R. Scott, Dinglong Huang -
https://arxiv.org/abs/1808.01340#deepmind: “A Short Note about Kinetics-600”, Joao Carreira, Eric Noland, Andras Banki-Horvath, Chloe Hillier, Andrew Zisserman -
2018-sharma.pdf#google: “Conceptual Captions: A Cleaned, Hypernymed, Image Alt-Text Dataset For Automatic Image Captioning”, Piyush Sharma, Nan Ding, Sebastian Goodman, Radu Soricut -
https://arxiv.org/abs/1805.00932#facebook: “Exploring the Limits of Weakly Supervised Pretraining”, -
https://arxiv.org/abs/1707.08819: “A Downsampled Variant of ImageNet As an Alternative to the CIFAR Datasets”, Patryk Chrabaszcz, Ilya Loshchilov, Frank Hutter -
https://arxiv.org/abs/1705.05640: “WebVision Challenge: Visual Learning and Understanding With Web Data”, Wen Li, Limin Wang, Wei Li, Eirikur Agustsson, Jesse Berent, Abhinav Gupta, Rahul Sukthankar, Luc Van Gool -
https://arxiv.org/abs/1704.05179: “SearchQA: A New Q&A Dataset Augmented With Context from a Search Engine”, Matthew Dunn, Levent Sagun, Mike Higgins, V. Ugur Guney, Volkan Cirik, Kyunghyun Cho -
http://www.lrec-conf.org/proceedings/lrec2014/pdf/1097_Paper.pdf: “N-Gram Counts and Language Models from the Common Crawl”, Christian Buck, Kenneth Heafield, Bas van Ooyen -
2011-torralba.pdf: “Unbiased Look at Dataset Bias”, Antonio Torralba, Alexei A. Efros