mlsanityData to 7 Oct 2026

A Survey on Automated Fact-Checking

arXiv 2108.11896 ↗First posted 26 Aug 2021cs.CLcited by 376

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Cited by 26 in LLM reasoning, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Fact-checking has become increasingly important due to the speed with which both information and misinformation can spread in the modern media ecosystem. Therefore, researchers have been exploring how fact-checking can be automated, using techniques based on natural language processing, machine learning, knowledge representation, and databases to automatically predict the veracity of claims. In this paper, we survey automated fact-checking stemming from natural language processing, and discuss its connections to related tasks and disciplines. In this process, we present an overview of existing datasets and models, aiming to unify the various definitions given and identify common concepts. Finally, we highlight challenges for future research.

Key facts. Cited by 376 papers in the map (data through 7 Oct 2026). Builds on 113 of its 217 references that are in the map. First posted on arXiv 26 Aug 2021.

Builds on

113 of 217 references.

References · 113

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38 more
  1. 2019Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inferencecited by 864
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  18. 2020Explainable Automated Fact-Checking for Public Health Claimscited by 209
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50 most cited of 113 All 113 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 30; 2023: 71; 2024: 103; 2025: 93; 2026: 75.

376 papers.

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  1. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,582
  2. 2023SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Modelscited by 787
  3. 2023CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquingcited by 751
  4. 2023Character-LLM: A Trainable Agent for Role-Playingcited by 335
  5. 2024Knowledge Conflicts for LLMs: A Surveycited by 274
  6. 2023RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Modelscited by 272
  7. 2023Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attackscited by 268
  8. 2023Can LLM-Generated Misinformation Be Detected?cited by 173
  9. 2024Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantificationcited by 159
  10. 2023Fact-Checking Complex Claims with Program-Guided Reasoningcited by 146
  11. 2023AVeriTeC: A Dataset for Real-world Claim Verification with Evidence from the Webcited by 144
  12. 2023Do Language Models Know When They're Hallucinating References?cited by 115
38 more
  1. 2022End-to-End Multimodal Fact-Checking and Explanation Generation: A Challenging Dataset and Modelscited by 105
  2. 2023ExpertQA: Expert-Curated Questions and Attributed Answerscited by 96
  3. 2023Fuzz4All: Universal Fuzzing with Large Language Modelscited by 88
  4. 2023Factcheck-Bench: Fine-Grained Evaluation Benchmark for Automatic Fact-checkerscited by 86
  5. 2022Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Surveycited by 80
  6. 2024LUQ: Long-text Uncertainty Quantification for LLMscited by 79
  7. 2023A Survey of Large Language Models Attributioncited by 71
  8. 2022Generating Literal and Implied Subquestions to Fact-check Complex Claimscited by 66
  9. 2024Factuality of Large Language Models: A Surveycited by 64
  10. 2024Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systemscited by 63
  11. 2024A Chain-of-Thought Is as Strong as Its Weakest Link: A Benchmark for Verifiers of Reasoning Chainscited by 63
  12. 2021DialFact: A Benchmark for Fact-Checking in Dialoguecited by 62
  13. 2024Factual Confidence of LLMs: on Reliability and Robustness of Current Estimatorscited by 59
  14. 2023FactKG: Fact Verification via Reasoning on Knowledge Graphscited by 57
  15. 2023Identifying and Mitigating Vulnerabilities in LLM-Integrated Applicationscited by 54
  16. 2023Scientific Fact-Checking: A Survey of Resources and Approachescited by 53
  17. 2023SCITAB: A Challenging Benchmark for Compositional Reasoning and Claim Verification on Scientific Tablescited by 51
  18. 2023Multimodal Automated Fact-Checking: A Surveycited by 51
  19. 2023Large Language Models Help Humans Verify Truthfulness -- Except When They Are Convincingly Wrongcited by 51
  20. 2023Do Large Language Models Know about Facts?cited by 49
  21. 2024Can LLMs Produce Faithful Explanations For Fact-checking? Towards Faithful Explainable Fact-Checking via Multi-Agent Debatecited by 48
  22. 2022Missing Counter-Evidence Renders NLP Fact-Checking Unrealistic for Misinformationcited by 45
  23. 2022Stretching Sentence-pair NLI Models to Reason over Long Documents and Clusterscited by 44
  24. 2023A New Benchmark and Reverse Validation Method for Passage-level Hallucination Detectioncited by 42
  25. 2023Exploring the Potential of Large Language Models in Computational Argumentationcited by 42
  26. 2021Attacking Open-domain Question Answering by Injecting Misinformationcited by 41
  27. 2022CHEF: A Pilot Chinese Dataset for Evidence-Based Fact-Checkingcited by 37
  28. 2023FactLLaMA: Optimizing Instruction-Following Language Models with External Knowledge for Automated Fact-Checkingcited by 37
  29. 2023Explainable Claim Verification via Knowledge-Grounded Reasoning with Large Language Modelscited by 37
  30. 2023HealthFC: Verifying Health Claims with Evidence-Based Medical Fact-Checkingcited by 35
  31. 2023Beyond the Chat: Executable and Verifiable Text-Editing with LLMscited by 35
  32. 2023Language Models Hallucinate, but May Excel at Fact Verificationcited by 35
  33. 2024Claim Detection for Automated Fact-checking: A Survey on Monolingual, Multilingual and Cross-Lingual Researchcited by 35
  34. 2023A Survey on Knowledge Editing of Neural Networkscited by 34
  35. 2025ReportBench: Evaluating Deep Research Agents via Academic Survey Taskscited by 34
  36. 2023chatClimate: Grounding Conversational AI in Climate Sciencecited by 32
  37. 2023Self-Checker: Plug-and-Play Modules for Fact-Checking with Large Language Modelscited by 31
  38. 2023The Perils & Promises of Fact-checking with Large Language Modelscited by 31

50 most cited of 376 All 376 (JSON)