“‘ML Dataset’ Tag”,2019-09-12 (Highleyman’s AI, GAN, instruct-tuning LLMs, AI scaling, dataset)⁠:

Bibliography for tag ai/dataset, most recent first: 5 related tags, 389 annotations, & 38 links (parent).

• See Also
• Links
  • “Do Large Language Models Perform Latent Multi-Hop Reasoning without Exploiting Shortcuts?”, Yang et al 2024
  • “HtmlRAG: HTML Is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems”, Tan et al 2024
  • “Centaur: a Foundation Model of Human Cognition”, Binz et al 2024
  • “SimpleStrat: Diversifying Language Model Generation With Stratification”, Wong et al 2024
  • “MLE-Bench: Evaluating Machine Learning Agents on Machine Learning Engineering”, Chan et al 2024
  • “Embodied Agent Interface: Benchmarking LLMs for Embodied Decision Making”, Li et al 2024
  • “Seeing Faces in Things: A Model and Dataset for Pareidolia”, Hamilton et al 2024
  • “H-ARC: A Robust Estimate of Human Performance on the Abstraction and Reasoning Corpus Benchmark”, LeGris et al 2024
  • “How to Evaluate Jailbreak Methods: A Case Study With the StrongREJECT Benchmark”, Bowen et al 2024
  • “To Code, or Not To Code? Exploring Impact of Code in Pre-Training”, Aryabumi et al 2024
  • “Tails Tell Tales: Chapter-Wide Manga Transcriptions With Character Names”, Sachdeva et al 2024
  • “ImagiNet: A Multi-Content Dataset for Generalizable Synthetic Image Detection via Contrastive Learning”, Boychev & Cholakov2024
  • “Me, Myself, and AI: The Situational Awareness Dataset (SAD) for LLMs”, Laine et al 2024
  • “Future Events As Backdoor Triggers: Investigating Temporal Vulnerabilities in LLMs”, Price et al 2024
  • “Sonnet or Not, Bot? Poetry Evaluation for Large Models and Datasets”, Walsh et al 2024
  • “APIGen: Automated Pipeline for Generating Verifiable and Diverse Function-Calling Datasets”, Liu et al 2024
  • “Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More?”, Lee et al 2024
  • “OlympicArena: Benchmarking Multi-Discipline Cognitive Reasoning for Superintelligent AI”, Huang et al 2024
  • “DataComp-LM: In Search of the next Generation of Training Sets for Language Models”, Li et al 2024
  • “GUI-WORLD: A Dataset for GUI-Oriented Multimodal LLM-Based Agents”, Chen et al 2024
  • “Newswire: A Large-Scale Structured Database of a Century of Historical News”, Silcock et al 2024
  • “Are We Done With MMLU?”, Gema et al 2024
  • “MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark”, Wang et al 2024
  • “LLMs Achieve Adult Human Performance on Higher-Order Theory of Mind Tasks”, Street et al 2024
  • “DeTikZify: Synthesizing Graphics Programs for Scientific Figures and Sketches With TikZ”, Belouadi et al 2024
  • “Sakuga-42M Dataset: Scaling Up Cartoon Research”, Pan et al 2024
  • “Can Language Models Explain Their Own Classification Behavior?”, Sherburn et al 2024
  • “Special Characters Attack: Toward Scalable Training Data Extraction From Large Language Models”, Bai et al 2024
  • “ImageInWords: Unlocking Hyper-Detailed Image Descriptions”, Garg et al 2024
  • “GSM1k: A Careful Examination of Large Language Model Performance on Grade School Arithmetic”, Zhang et al 2024
  • “Building a Large Japanese Web Corpus for Large Language Models”, Okazaki et al 2024
  • “CulturalTeaming: AI-Assisted Interactive Red-Teaming for Challenging LLMs’ (Lack Of) Multicultural Knowledge”, Chiu et al 2024
  • “VisualWebBench: How Far Have Multimodal LLMs Evolved in Web Page Understanding and Grounding?”, Liu et al 2024
  • “Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators”, Dubois 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
  • “Long-Form Factuality in Large Language Models”, Wei 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
  • “Investigating Continual Pretraining in Large Language Models: Insights and Implications”, Yıldız et al 2024
  • “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
  • “DE-COP: Detecting Copyrighted Content in Language Models Training Data”, Duarte et al 2024
  • “I Think, Therefore I Am: Benchmarking Awareness of Large Language Models Using AwareBench”, Li et al 2024
  • “Can AI Assistants Know What They Don’t Know?”, Cheng 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
  • “Large Language Models Play StarCraft II: Benchmarks and A Chain of Summarization Approach”, Ma 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
  • “EQ-Bench: An Emotional Intelligence Benchmark for Large Language Models”, Paech2023
  • “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
  • “Dazed & Confused: A Large-Scale Real-World User Study of ReCAPTCHAv2”, Searles et al 2023
  • “Instruction-Following Evaluation for Large Language Models”, Zhou et al 2023
  • “In Search of the Long-Tail: Systematic Generation of Long-Tail Inferential Knowledge via Logical Rule Guided Search”, Li 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
  • “FANToM: A Benchmark for Stress-Testing Machine Theory of Mind in Interactions”, Kim et al 2023
  • “MuSR: Testing the Limits of Chain-Of-Thought With Multistep Soft Reasoning”, Sprague et al 2023
  • “Ignore This Title and HackAPrompt: Exposing Systemic Vulnerabilities of LLMs through a Global Scale Prompt Hacking Competition”, Schulhoff et al 2023
  • “Llemma: An Open Language Model For Mathematics”, Azerbayev 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
  • “SWE-Bench: Can Language Models Resolve Real-World GitHub Issues?”, Jimenez et al 2023
  • “OpenWebMath: An Open Dataset of High-Quality Mathematical Web Text”, Paster et al 2023
  • “FreshLLMs: Refreshing Large Language Models With Search Engine Augmentation”, Vu et al 2023
  • “UltraFeedback: Boosting Language Models With High-Quality Feedback”, Cui et al 2023
  • “MTOB: A Benchmark for Learning to Translate a New Language from One Grammar Book”, Tanzer et al 2023
  • “Demystifying CLIP Data”, Xu et al 2023
  • “The Cambridge Law Corpus: A Corpus for Legal AI Research”, Östling et al 2023
  • “MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models”, Yu et al 2023
  • “LongLoRA: Efficient Fine-Tuning of Long-Context Large Language Models”, Chen et al 2023
  • “SlimPajama-DC: Understanding Data Combinations for LLM Training”, Shen et al 2023
  • “MADLAD-400: A Multilingual And Document-Level Large Audited Dataset”, Kudugunta et al 2023
  • “GoodWiki”, Choi2023
  • “From Sparse to Dense: GPT-4 Summarization With Chain of Density (CoD) Prompting”, Adams et al 2023
  • “FIMO: A Challenge Formal Dataset for Automated Theorem Proving”, Liu et al 2023
  • “American Stories: A Large-Scale Structured Text Dataset of Historical U.S. Newspapers”, Dell et al 2023
  • “LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models”, Guha et al 2023
  • “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
  • “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
  • “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 & Dell2023
  • “LeanDojo: Theorem Proving With Retrieval-Augmented Language Models”, Yang et al 2023
  • “SugarCrepe: Fixing Hackable Benchmarks for Vision-Language Compositionality”, Hsieh et al 2023
  • “ARIES: A Corpus of Scientific Paper Edits Made in Response to Peer Reviews”, D’Arcy et al 2023
  • “Understanding Social Reasoning in Language Models With Language Models”, Gandhi et al 2023
  • “OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents”, Laurençon et al 2023
  • “AI Is a Lot of Work: As the Technology Becomes Ubiquitous, a Vast Tasker Underclass Is Emerging—And Not Going Anywhere”, Dzieza2023
  • “Anime Character Identification and Tag Prediction by Multimodality Modeling: Dataset and Model”, Yi et al 2023
  • “ChessGPT: Bridging Policy Learning and Language Modeling”, Feng et al 2023
  • “Why YouTube Could Give Google an Edge in AI”, Victor2023
  • “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
  • “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 & Li2023
  • “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
  • “How Well Do Large Language Models Perform in Arithmetic Tasks?”, Yuan et al 2023
  • “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 & Federmann2023
  • “Benchmarks for Automated Commonsense Reasoning: A Survey”, Davis2023
  • “Data Selection for Language Models via Importance Resampling”, Xie et al 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
  • “Unnatural Instructions: Tuning Language Models With (Almost) No Human Labor”, Honovich et al 2022
  • “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
  • “Text Embeddings by Weakly-Supervised Contrastive Pre-Training”, Wang et al 2022
  • “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
  • “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
  • “Will We Run out of Data? An Analysis of the Limits of Scaling Datasets in Machine Learning”, Villalobos et al 2022
  • “Large Language Models Can Self-Improve”, Huang et al 2022
  • “CARP: Robust Preference Learning for Storytelling via Contrastive Reinforcement Learning”, Castricato et al 2022
  • “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
  • “Dynamic Prompt Learning via Policy Gradient for Semi-Structured Mathematical Reasoning”, Lu et al 2022
  • “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
  • “Bugs in the Data: How ImageNet Misrepresents Biodiversity”, Luccioni & Rolnick2022
  • “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 & Liu2022
  • “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 & Neubig2022
  • “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
  • “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
  • “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
  • “Data Determines Distributional Robustness in Contrastive Language Image Pre-Training (CLIP)”, Fang et al 2022
  • “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
  • “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
  • “ByT5 Model for Massively Multilingual Grapheme-To-Phoneme Conversion”, Zhu et al 2022
  • “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
  • “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
  • “WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation”, Liu et al 2022
  • “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
  • “ERNIE-ViLG: Unified Generative Pre-Training for Bidirectional Vision-Language Generation”, Zhang et al 2021
  • “A Fistful of Words: Learning Transferable Visual Models from Bag-Of-Words Supervision”, Tejankar et al 2021
  • “GLIDE: Towards Photorealistic Image Generation and Editing With Text-Guided Diffusion Models”, Nichol et al 2021
  • “QuALITY: Question Answering With Long Input Texts, Yes!”, Pang et al 2021
  • “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
  • “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
  • “MAD: A Scalable Dataset for Language Grounding in Videos from Movie Audio Descriptions”, Soldan et al 2021
  • “BASIC: Combined Scaling for Open-Vocabulary Image Classification”, Pham et al 2021
  • “It’s About Time: Analog Clock Reading in the Wild”, Yang 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
  • “RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement Learning”, Ramos et al 2021
  • “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
  • “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
  • “T0: Multitask Prompted Training Enables Zero-Shot Task Generalization”, Sanh et al 2021
  • “Can Machines Learn Morality? The Delphi Experiment”, Jiang et al 2021
  • “Situated Dialogue Learning through Procedural Environment Generation”, Ammanabrolu et al 2021
  • “MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research”, Samvelyan et al 2021
  • “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
  • “LAION-400-Million Open Dataset”, Schuhmann2021
  • “Transfer Learning for Pose Estimation of Illustrated Characters”, Chen & Zwicker2021
  • “MuSiQue: Multi-Hop Questions via Single-Hop Question Composition”, Trivedi et al 2021
  • “Scaling Vision Transformers”, Zhai et al 2021
  • “QASPER: A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers”, Dasigi et al 2021
  • “XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyond”, Barbieri et al 2021
  • “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
  • “A Massive 7T FMRI Dataset to Bridge Cognitive and Computational Neuroscience”, Allen et al 2021
  • “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
  • “Automatic Curation of Large-Scale Datasets for Audio-Visual Representation Learning”, Lee et al 2021
  • “MSR-VTT: A Large Video Description Dataset for Bridging Video and Language”, Xu et al 2021
  • “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
  • “Selective Eye-Gaze Augmentation To Enhance Imitation Learning In Atari Games”, Thammineni et al 2020
  • “VoxLingua107: a Dataset for Spoken Language Recognition”, Valk & Alumäe2020
  • “MoGaze: A Dataset of Full-Body Motions That Includes Workspace Geometry and Eye-Gaze”, Kratzer et al 2020
  • “End-To-End Chinese Landscape Painting Creation Using Generative Adversarial Networks”, Xue2020
  • “Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding”, Roberts et al 2020
  • “Constructing A Multi-Hop QA Dataset for Comprehensive Evaluation of Reasoning Steps”, Ho et al 2020
  • “Language ID in the Wild: Unexpected Challenges on the Path to a Thousand-Language Web Text Corpus”, Caswell et al 2020
  • “Open-Domain Question Answering Goes Conversational via Question Rewriting”, Anantha et al 2020
  • “Digital Voicing of Silent Speech”, Gaddy & Klein2020
  • “A C/C++ Code Vulnerability Dataset With Code Changes and CVE Summaries”, Fan et al 2020
  • “MMLU: Measuring Massive Multitask Language Understanding”, Hendrycks et al 2020
  • “ETHICS: Aligning AI With Shared Human Values”, Hendrycks et al 2020
  • “Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing”, Gu et al 2020
  • “CoVoST 2 and Massively Multilingual Speech-To-Text Translation”, Wang et al 2020
  • “The Many Faces of Robustness: A Critical Analysis of Out-Of-Distribution Generalization”, Hendrycks et al 2020
  • “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
  • “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
  • “SAYCam: A Large, Longitudinal Audiovisual Dataset Recorded from the Infant’s Perspective”, Sullivan et al 2020
  • “ImageNet-A: Natural Adversarial Examples”, Hendrycks et al 2020
  • “Measuring Compositional Generalization: A Comprehensive Method on Realistic Data”, Keysers et al 2019
  • “Libri-Light: A Benchmark for ASR With Limited or No Supervision”, Kahn et al 2019
  • “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”, Nguyen2019
  • “How Machine Learning Can Help Unlock the World of Ancient Japan”, Lamb2019
  • “Compressive Transformers for Long-Range Sequence Modeling”, Rae et al 2019
  • “CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning”, Lin et al 2019
  • “CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data”, Wenzek et al 2019
  • “T5: Exploring the Limits of Transfer Learning With a Unified Text-To-Text Transformer”, Raffel et al 2019
  • “Restoring Ancient Text Using Deep Learning (Pythia): a Case Study on Greek Epigraphy”, Assael et al 2019
  • “CATER: A Diagnostic Dataset for Compositional Actions and TEmporal Reasoning”, Girdhar & Ramanan2019
  • “PubMedQA: A Dataset for Biomedical Research Question Answering”, Jin et al 2019
  • “ObjectNet: A Large-Scale Bias-Controlled Dataset for Pushing the Limits of Object Recognition Models”, Barbu et al 2019
  • “No Press Diplomacy: Modeling Multi-Agent Gameplay”, Paquette et al 2019
  • “Language Modeling State-Of-The-Art Leaderboards”, paperswithcode.com 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
  • “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
  • “ImageNet-Sketch: Learning Robust Global Representations by Penalizing Local Predictive Power”, Wang et al 2019
  • “Cold Case: The Lost MNIST Digits”, Yadav & Bottou2019
  • “SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems”, Wang et al 2019
  • “The MineRL 2019 Competition on Sample Efficient Reinforcement Learning Using Human Priors”, Guss et al 2019
  • “ProductNet: a Collection of High-Quality Datasets for Product Representation Learning”, Wang et al 2019
  • “Benchmarking Neural Network Robustness to Common Corruptions and Perturbations”, Hendrycks & Dietterich2019
  • “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
  • “DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs”, Dua et al 2019
  • “A Replication Study: Machine Learning Models Are Capable of Predicting Sexual Orientation From Facial Images”, Leuner2019
  • “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 & Denzler2019
  • “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 & Demers2019
  • “Natural Questions: A Benchmark for Question Answering Research”, Kwiatkowski et al 2019
  • “A Style-Based Generator Architecture for Generative Adversarial Networks”, Karras et al 2018
  • “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
  • “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
  • “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
  • “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 & Dietterich2018
  • “Conceptual Captions: A Cleaned, Hypernymed, Image Alt-Text Dataset For Automatic Image Captioning”, Sharma et al 2018
  • “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
  • “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
  • “GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding”, Wang et al 2018
  • “The Sound of Pixels”, Zhao et al 2018
  • “FEVER: a Large-Scale Dataset for Fact Extraction and VERification”, Thorne et al 2018
  • “Think You Have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge”, Clark et al 2018
  • “SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction”, Liang et al 2018
  • “11K Hands: Gender Recognition and Biometric Identification Using a Large Dataset of Hand Images”, Afifi2017
  • “Progressive Growing of GANs for Improved Quality, Stability, and Variation”, Karras et al 2017
  • “OpenML Benchmarking Suites”, Bischl et al 2017
  • “WebVision Database: Visual Learning and Understanding from Web Data”, Li et al 2017
  • “A Downsampled Variant of ImageNet As an Alternative to the CIFAR Datasets”, Chrabaszcz et al 2017
  • “Revisiting Unreasonable Effectiveness of Data in Deep Learning Era”, Sun et al 2017
  • “Driver Identification Using Automobile Sensor Data from a Single Turn”, Hallac et al 2017
  • “StreetStyle: Exploring World-Wide Clothing Styles from Millions of Photos”, Matzen et al 2017
  • “The Kinetics Human Action Video Dataset”, Kay et al 2017
  • “WebVision Challenge: Visual Learning and Understanding With Web Data”, Li et al 2017
  • “TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension”, Joshi et al 2017
  • “Dense-Captioning Events in Videos”, Krishna et al 2017
  • “BAM! The Behance Artistic Media Dataset for Recognition Beyond Photography”, Wilber et al 2017
  • “SearchQA: A New Q&A Dataset Augmented With Context from a Search Engine”, Dunn et al 2017
  • “RACE: Large-Scale ReAding Comprehension Dataset From Examinations”, Lai et al 2017
  • “NewsQA: A Machine Comprehension Dataset”, Trischler et al 2016
  • “MS MARCO: A Human Generated MAchine Reading COmprehension Dataset”, Bajaj et al 2016
  • “Lip Reading Sentences in the Wild”, Chung et al 2016
  • “Pointer Sentinel Mixture Models”, Merity et al 2016
  • “Deep Learning the City: Quantifying Urban Perception At A Global Scale”, Dubey et al 2016
  • “Solving General Arithmetic Word Problems”, Roy & Roth2016
  • “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
  • “Matching Networks for One Shot Learning”, Vinyals et al 2016
  • “Convolutional Sketch Inversion”, Güçlütürk et al 2016
  • “The MovieLens Datasets: History and Context”, Harper & Konstan2015
  • “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”, Resig2013
  • “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 & Efros2011
  • “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
  • “About the Test Data”
  • “DataGemma: AI Open Models Connecting LLMs to Google’s Data Commons”
  • “Scale AI Secures $1B Funding at $14B Valuation As Its CEO Predicts Big Revenue Growth and Profitability by Year-End [On Very High Quality Data]”
  • “No Robots: Look Ma, an Instruction Dataset That Wasn’t Generated by GPTs!”, HuggingFace2024
  • “Psych-101 Dataset [For Centaur]”
  • “FineWeb: Decanting the Web for the Finest Text Data at Scale”
  • “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”
  • “Lip Reading Sentences in the Wild [Video]”
  • Sort By Magic
    • model-evaluation bioinformatics theorem-proving generative-art cognition-verification data-scaling
    • reasoning-challenges
    • visual-language
    • dataset-robustness
    • question-answering
  • Wikipedia
• Miscellaneous
• Bibliography