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Explainable Automated Fact-Checking for Public Health Claims

arXiv 2010.09926 ↗First posted 19 Oct 2020cs.CL cs.AIcited by 211

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Abstract (arXiv)

Fact-checking is the task of verifying the veracity of claims by assessing their assertions against credible evidence. The vast majority of fact-checking studies focus exclusively on political claims. Very little research explores fact-checking for other topics, specifically subject matters for which expertise is required. We present the first study of explainable fact-checking for claims which require specific expertise. For our case study we choose the setting of public health. To support this case study we construct a new dataset PUBHEALTH of 11.8K claims accompanied by journalist crafted, gold standard explanations (i.e., judgments) to support the fact-check labels for claims. We explore two tasks: veracity prediction and explanation generation. We also define and evaluate, with humans and computationally, three coherence properties of explanation quality. Our results indicate that, by training on in-domain data, gains can be made in explainable, automated fact-checking for claims which require specific expertise.

Key facts. Cited by 211 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Oct 2020.

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Citing papers per year: 2021: 22; 2022: 27; 2023: 43; 2024: 48; 2025: 40; 2026: 31.

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  1. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
  2. 2021MetaICL: Learning to Learn In Contextcited by 454
  3. 2021A Survey on Automated Fact-Checkingcited by 378
  4. 2025Agentic Retrieval-Augmented Generation: A Survey on Agentic RAGcited by 249
  5. 2021FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured informationcited by 167
  6. 2023Fact-Checking Complex Claims with Program-Guided Reasoningcited by 146
  7. 2021MultiVerS: Improving scientific claim verification with weak supervision and full-document contextcited by 143
  8. 2021CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLPcited by 142
  9. 2021Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processingcited by 126
  10. 2022End-to-End Multimodal Fact-Checking and Explanation Generation: A Challenging Dataset and Modelscited by 107
  11. 2023Towards LLM-based Fact Verification on News Claims with a Hierarchical Step-by-Step Prompting Methodcited by 97
  12. 2022SciFact-Open: Towards open-domain scientific claim verificationcited by 94
38 more
  1. 2024Medical Graph RAG: Towards Safe Medical Large Language Model via Graph Retrieval-Augmented Generationcited by 90
  2. 2024Med42-v2: A Suite of Clinical LLMscited by 90
  3. 2021e-ViL: A Dataset and Benchmark for Natural Language Explanations in Vision-Language Taskscited by 84
  4. 2021Few-Shot Self-Rationalization with Natural Language Promptscited by 83
  5. 2023Automatic Evaluation of Attribution by Large Language Modelscited by 72
  6. 2022Generating Literal and Implied Subquestions to Fact-check Complex Claimscited by 66
  7. 2022Generating Scientific Claims for Zero-Shot Scientific Fact Checkingcited by 59
  8. 2023FactKG: Fact Verification via Reasoning on Knowledge Graphscited by 57
  9. 2023HAGRID: A Human-LLM Collaborative Dataset for Generative Information-Seeking with Attributioncited by 56
  10. 2023Scientific Fact-Checking: A Survey of Resources and Approachescited by 54
  11. 2021ProoFVer: Natural Logic Theorem Proving for Fact Verificationcited by 53
  12. 2023Multimodal Automated Fact-Checking: A Surveycited by 53
  13. 2023Do Large Language Models Know about Facts?cited by 51
  14. 2024Explainable Fake News Detection With Large Language Model via Defense Among Competing Wisdomcited by 50
  15. 2024Can LLMs Produce Faithful Explanations For Fact-checking? Towards Faithful Explainable Fact-Checking via Multi-Agent Debatecited by 48
  16. 2023LongForm: Effective Instruction Tuning with Reverse Instructionscited by 47
  17. 2024MARS: Meaning-Aware Response Scoring for Uncertainty Estimation in Generative LLMscited by 46
  18. 2022Missing Counter-Evidence Renders NLP Fact-Checking Unrealistic for Misinformationcited by 45
  19. 2025Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMscited by 42
  20. 2024Towards Evaluating and Building Versatile Large Language Models for Medicinecited by 40
  21. 2022CHEF: A Pilot Chinese Dataset for Evidence-Based Fact-Checkingcited by 37
  22. 2023FactLLaMA: Optimizing Instruction-Following Language Models with External Knowledge for Automated Fact-Checkingcited by 37
  23. 2023Explainable Claim Verification via Knowledge-Grounded Reasoning with Large Language Modelscited by 37
  24. 2023Language Models Hallucinate, but May Excel at Fact Verificationcited by 37
  25. 2022BigBIO: A Framework for Data-Centric Biomedical Natural Language Processingcited by 36
  26. 2023EX-FEVER: A Dataset for Multi-hop Explainable Fact Verificationcited by 36
  27. 2021Learn Continually, Generalize Rapidly: Lifelong Knowledge Accumulation for Few-shot Learningcited by 35
  28. 2023Reading and Reasoning over Chart Images for Evidence-based Automated Fact-Checkingcited by 35
  29. 2023HealthFC: Verifying Health Claims with Evidence-Based Medical Fact-Checkingcited by 35
  30. 2024Claim Detection for Automated Fact-checking: A Survey on Monolingual, Multilingual and Cross-Lingual Researchcited by 35
  31. 2023SAIL: Search-Augmented Instruction Learningcited by 33
  32. 2024A Preliminary Study of o1 in Medicine: Are We Closer to an AI Doctor?cited by 33
  33. 2023Split-and-Denoise: Protect large language model inference with local differential privacycited by 32
  34. 2021Local Interpretations for Explainable Natural Language Processing: A Surveycited by 31
  35. 2021On the Diversity and Limits of Human Explanationscited by 30
  36. 2024JustiLM: Few-shot Justification Generation for Explainable Fact-Checking of Real-world Claimscited by 29
  37. 2025Show Me the Work: Fact-Checkers' Requirements for Explainable Automated Fact-Checkingcited by 29
  38. 2025Worse than Zero-shot? A Fact-Checking Dataset for Evaluating the Robustness of RAG Against Misleading Retrievalscited by 28

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