NAACL 2024long99 citations

CMB: A Comprehensive Medical Benchmark in Chinese

Xidong Wang, Guiming Chen, Song Dingjie, Zhang Zhiyi, Zhihong Chen, Qingying Xiao, Junying Chen, Feng Jiang

Abstract

Large Language Models (LLMs) provide a possibility to make a great breakthrough in medicine. The establishment of a standardized medical benchmark becomes a fundamental cornerstone to measure progression. However, medical environments in different regions have their local characteristics, e.g., the ubiquity and significance of traditional Chinese medicine within China. Therefore, merely translating English-based medical evaluation may result in contextual incongruities to a local region. To solve the issue, we propose a localized medical benchmark called CMB, a Comprehensive Medical Benchmark in Chinese, designed and rooted entirely within the native Chinese linguistic and cultural framework. While traditional Chinese medicine is integral to this evaluation, it does not constitute its entirety. Using this benchmark, we have evaluated several prominent large-scale LLMs, including ChatGPT, GPT-4, dedicated Chinese LLMs, and LLMs specialized in the medical domain. We hope this benchmark provide first-hand experience in existing LLMs for medicine and also facilitate the widespread adoption and enhancement of medical LLMs within China. Our data and code are publicly available at https://github.com/FreedomIntelligence/CMB.

BibTeX
@inproceedings{wang-etal-2024-cmb,
    title = "{CMB}: A Comprehensive Medical Benchmark in {C}hinese",
    author = "Wang, Xidong  and
      Chen, Guiming  and
      Dingjie, Song  and
      Zhiyi, Zhang  and
      Chen, Zhihong  and
      Xiao, Qingying  and
      Chen, Junying  and
      Jiang, Feng  and
      Li, Jianquan  and
      Wan, Xiang  and
      Wang, Benyou  and
      Li, Haizhou",
    editor = "Duh, Kevin  and
      Gomez, Helena  and
      Bethard, Steven",
    booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.naacl-long.343/",
    doi = "10.18653/v1/2024.naacl-long.343",
    pages = "6184--6205"
}