mlsanityData to 7 Oct 2026

TabFact: A Large-scale Dataset for Table-based Fact Verification

arXiv 1909.02164 ↗First posted 5 Sep 2019cs.CL cs.AIcited by 425

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Cited by 82 in LLM reasoning, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

The problem of verifying whether a textual hypothesis holds based on the given evidence, also known as fact verification, plays an important role in the study of natural language understanding and semantic representation. However, existing studies are mainly restricted to dealing with unstructured evidence (e.g., natural language sentences and documents, news, etc), while verification under structured evidence, such as tables, graphs, and databases, remains under-explored. This paper specifically aims to study the fact verification given semi-structured data as evidence. To this end, we construct a large-scale dataset called TabFact with 16k Wikipedia tables as the evidence for 118k human-annotated natural language statements, which are labeled as either ENTAILED or REFUTED. TabFact is challenging since it involves both soft linguistic reasoning and hard symbolic reasoning. To address these reasoning challenges, we design two different models: Table-BERT and Latent Program Algorithm (LPA). Table-BERT leverages the state-of-the-art pre-trained language model to encode the linearized tables and statements into continuous vectors for verification. LPA parses statements into programs and executes them against the tables to obtain the returned binary value for verification. Both methods achieve similar accuracy but still lag far behind human performance. We also perform a comprehensive analysis to demonstrate great future opportunities. The data and code of the dataset are provided in urlhttps://github.com/wenhuchen/Table-Fact-Checking.

Key facts. Cited by 425 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Sep 2019.

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Citing papers per year: 2021: 50; 2022: 43; 2023: 55; 2024: 98; 2025: 112; 2026: 67.

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  1. 2024MiniCPM-V: A GPT-4V Level MLLM on Your Phonecited by 1,061
  2. 2021FinQA: A Dataset of Numerical Reasoning over Financial Datacited by 552
  3. 2021MetaICL: Learning to Learn In Contextcited by 450
  4. 2021A Survey on Automated Fact-Checkingcited by 376
  5. 2023Monkey: Image Resolution and Text Label Are Important Things for Large Multi-modal Modelscited by 356
  6. 2023StructGPT: A General Framework for Large Language Model to Reason over Structured Datacited by 339
  7. 2022UL2: Unifying Language Learning Paradigmscited by 292
  8. 2021TAPEX: Table Pre-training via Learning a Neural SQL Executorcited by 233
  9. 2022UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Modelscited by 231
  10. 2022Binding Language Models in Symbolic Languagescited by 224
  11. 2021Get Your Vitamin C! Robust Fact Verification with Contrastive Evidencecited by 222
  12. 2024mPLUG-DocOwl 1.5: Unified Structure Learning for OCR-free Document Understandingcited by 220
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  2. 2024Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understandingcited by 185
  3. 2021MultiModalQA: Complex Question Answering over Text, Tables and Imagescited by 177
  4. 2023Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoningcited by 177
  5. 2021FeTaQA: Free-form Table Question Answeringcited by 174
  6. 2025MiniCPM-V 4.5: Cooking Efficient MLLMs via Architecture, Data, and Training Recipecited by 174
  7. 2023mPLUG-DocOwl: Modularized Multimodal Large Language Model for Document Understandingcited by 167
  8. 2021FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured informationcited by 166
  9. 2024Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Surveycited by 157
  10. 2021Automated Fact-Checking for Assisting Human Fact-Checkerscited by 144
  11. 2023UReader: Universal OCR-free Visually-situated Language Understanding with Multimodal Large Language Modelcited by 143
  12. 2021CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLPcited by 142
  13. 2022Unifying Vision, Text, and Layout for Universal Document Processingcited by 140
  14. 2023TableLlama: Towards Open Large Generalist Models for Tablescited by 136
  15. 2024SPHINX-X: Scaling Data and Parameters for a Family of Multi-modal Large Language Modelscited by 130
  16. 2024TableBench: A Comprehensive and Complex Benchmark for Table Question Answeringcited by 127
  17. 2022Large Language Models are few(1)-shot Table Reasonerscited by 116
  18. 2023Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Studycited by 116
  19. 2024mPLUG-DocOwl2: High-resolution Compressing for OCR-free Multi-page Document Understandingcited by 113
  20. 2023DocLLM: A layout-aware generative language model for multimodal document understandingcited by 112
  21. 2022End-to-End Multimodal Fact-Checking and Explanation Generation: A Challenging Dataset and Modelscited by 105
  22. 2023KOSMOS-2.5: A Multimodal Literate Modelcited by 95
  23. 2021X-FACT: A New Benchmark Dataset for Multilingual Fact Checkingcited by 90
  24. 2022TableFormer: Robust Transformer Modeling for Table-Text Encodingcited by 90
  25. 2024LLaVA-MoLE: Sparse Mixture of LoRA Experts for Mitigating Data Conflicts in Instruction Finetuning MLLMscited by 84
  26. 2025VTool-R1: VLMs Learn to Think with Images via Reinforcement Learning on Multimodal Tool Usecited by 84
  27. 2022MapQA: A Dataset for Question Answering on Choropleth Mapscited by 82
  28. 2025ReFocus: Visual Editing as a Chain of Thought for Structured Image Understandingcited by 81
  29. 2021A Survey on Stance Detection for Mis- and Disinformation Identificationcited by 75
  30. 2024SPIQA: A Dataset for Multimodal Question Answering on Scientific Paperscited by 75
  31. 2024MM1.5: Methods, Analysis & Insights from Multimodal LLM Fine-tuningcited by 75
  32. 2024TabPedia: Towards Comprehensive Visual Table Understanding with Concept Synergycited by 70
  33. 2023Table-GPT: Table-tuned GPT for Diverse Table Taskscited by 69
  34. 2024TableGPT2: A Large Multimodal Model with Tabular Data Integrationcited by 69
  35. 2024TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domainscited by 68
  36. 2024A Survey on Employing Large Language Models for Text-to-SQL Taskscited by 67
  37. 2022Generating Literal and Implied Subquestions to Fact-check Complex Claimscited by 66
  38. 2021DialFact: A Benchmark for Fact-Checking in Dialoguecited by 62

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