mlsanityData to 14 Oct 2026

MedFact: A Large-scale Chinese Dataset for Evidence-based Medical Fact-checking of LLM Responses

arXiv 2509.17436 ↗First posted 22 Sep 2025cs.CLcited by 6

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Abstract (arXiv)

Medical fact-checking has become increasingly critical as more individuals seek medical information online. However, existing datasets predominantly focus on human-generated content, leaving the verification of content generated by large language models (LLMs) relatively unexplored. To address this gap, we introduce MedFact, the first evidence-based Chinese medical fact-checking dataset of LLM-generated medical content. It consists of 1,321 questions and 7,409 claims, mirroring the complexities of real-world medical scenarios. We conduct comprehensive experiments in both in-context learning (ICL) and fine-tuning settings, showcasing the capability and challenges of current LLMs on this task, accompanied by an in-depth error analysis to point out key directions for future research. Our dataset is publicly available at https://github.com/AshleyChenNLP/MedFact.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 13 of its 27 references that are in the map. First posted on arXiv 22 Sep 2025.

Builds on

13 of 27 references.

References · 13

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2024DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Modelcited by 1,135
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  10. 2023When does In-context Learning Fall Short and Why? A Study on Specification-Heavy Taskscited by 37
  11. 2023HealthFC: Verifying Health Claims with Evidence-Based Medical Fact-Checkingcited by 35
  12. 2022CoVERT: A Corpus of Fact-checked Biomedical COVID-19 Tweetscited by 29
1 more
  1. 2024Guardians of Discourse: Evaluating LLMs on Multilingual Offensive Language Detectioncited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 3.

6 papers.