@inproceedings{wang-etal-2025-huatuo,
    title = "Huatuo-26{M}, a Large-scale {C}hinese Medical {QA} Dataset",
    author = "Wang, Xidong  and
      Li, Jianquan  and
      Chen, Shunian  and
      Zhu, Yuxuan  and
      Wu, Xiangbo  and
      Zhang, Zhiyi  and
      Xu, Xiaolong  and
      Chen, Junying  and
      Fu, Jie  and
      Wan, Xiang  and
      Gao, Anningzhe  and
      Wang, Benyou",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.findings-naacl.211/",
    pages = "3828--3848",
    ISBN = "979-8-89176-195-7",
    abstract = "Large Language Models infuse newfound vigor into the advancement of the medical domain, yet the scarcity of data poses a significant bottleneck hindering community progress. In this paper, we release the largest ever medical Question Answering (QA) dataset with 26 Million QA pairs named Huatuo-26M. We benchmark many existing approaches in our dataset in terms of both retrieval and generation. We also experimentally show the benefit of the proposed dataset in many aspects: (i) it serves as a fine-tuning data for training medical Large Language Models (LLMs); (ii) it works as an external knowledge source for retrieval-augmented generation (RAG); (iii) it demonstrates transferability by enhancing zero-shot performance on other QA datasets; and (iv) it aids in training biomedical model as a pre-training corpus. Our empirical findings substantiate the dataset{'}s utility in these domains, thereby confirming its significance as a resource in the medical QA landscape."
}