Huatuo-26M, a Large-scale Chinese Medical QA Dataset
Xidong Wang, Jianquan Li, Shunian Chen, Yuxuan Zhu, Xiangbo Wu, Zhiyi Zhang, Xiaolong Xu, Junying Chen
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.
BibTeX
@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://aclanthology.org/2025.findings-naacl.211/",
pages = "3828--3848",
ISBN = "979-8-89176-195-7"
}