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

  • Xi'an Jiaotong-Liverpool University
  • University of Liverpool

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

4 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationEMNLP 2025 - 2025 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference
EditorsChristos Christodoulopoulos, Tanmoy Chakraborty, Carolyn Rose, Violet Peng
PublisherAssociation for Computational Linguistics (ACL)
Pages32340-32353
Number of pages14
ISBN (Electronic)9798891763326
DOIs
Publication statusPublished - 2025
Event30th Conference on Empirical Methods in Natural Language Processing, EMNLP 2025 - Suzhou, China
Duration: 4 Nov 20259 Nov 2025

Publication series

NameEMNLP 2025 - 2025 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference

Conference

Conference30th Conference on Empirical Methods in Natural Language Processing, EMNLP 2025
Country/TerritoryChina
CitySuzhou
Period4/11/259/11/25

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