MedFact: A Large-scale Chinese Dataset for Evidence-based Medical Fact-checking of LLM Responses
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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
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Citing papers per year: 2025: 3; 2026: 3.
6 papers.
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- 2026PsychEthicsBench: Evaluating Large Language Models Against Australian Mental Health Ethicscited by 4
- 2026Overview of the PsyDefDetect Shared Task at BioNLP 2026: Detecting Levels of Psychological Defense Mechanisms in Supportive Conversationscited by 3
- 2025ArchMap: Arch-Flattening and Knowledge-Guided Vision Language Model for Tooth Counting and Structured Dental Understandingcited by 2
- 2025Evaluating Medical LLMs by Levels of Autonomy: A Survey Moving from Benchmarks to Applicationscited by 0
- 2026SAFARI: An Industrial Benchmark for LLM-Assisted Hazard Analysis and Risk Assessmentcited by 0
- 2026SAFARI: An Industrial Benchmark for LLM-Assisted Hazard Analysis and Risk Assessment17 Sep 2026
- 2026Overview of the PsyDefDetect Shared Task at BioNLP 2026: Detecting Levels of Psychological Defense Mechanisms in Supportive Conversations24 May 2026
- 2026PsychEthicsBench: Evaluating Large Language Models Against Australian Mental Health Ethics7 Jan 2026
- 2025You Never Know a Person, You Only Know Their Defenses: Detecting Levels of Psychological Defense Mechanisms in Supportive Conversations17 Dec 2025
- 2025ArchMap: Arch-Flattening and Knowledge-Guided Vision Language Model for Tooth Counting and Structured Dental Understanding18 Nov 2025
- 2025Evaluating Medical LLMs by Levels of Autonomy: A Survey Moving from Benchmarks to Applications20 Oct 2025