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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2509.17436 |
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| _version_ | 1866909799501791232 |
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| author | Chen, Tong Wang, Zimu Miao, Yiyi Luo, Haoran Sun, Yuanfei Wang, Wei Jiang, Zhengyong Sen, Procheta Su, Jionglong |
| author_facet | Chen, Tong Wang, Zimu Miao, Yiyi Luo, Haoran Sun, Yuanfei Wang, Wei Jiang, Zhengyong Sen, Procheta Su, Jionglong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_17436 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MedFact: A Large-scale Chinese Dataset for Evidence-based Medical Fact-checking of LLM Responses Chen, Tong Wang, Zimu Miao, Yiyi Luo, Haoran Sun, Yuanfei Wang, Wei Jiang, Zhengyong Sen, Procheta Su, Jionglong Computation and Language 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. |
| title | MedFact: A Large-scale Chinese Dataset for Evidence-based Medical Fact-checking of LLM Responses |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2509.17436 |