@inproceedings{wang-etal-2025-ccl25-evalren,
    title = "{CCL}25-Eval任务9总结报告:中医辨证辨病及中药处方生成评测",
    author = "Wang, Cong  and
      Zhao, Zhizhuo  and
      Li, Yishuo  and
      Guan, Hongjiao  and
      Wang, Yifei  and
      Li, Zhenyu  and
      Lu, Wenpeng",
    editor = "Lin, Hongfei  and
      Li, Bin  and
      Tan, Hongye",
    booktitle = "Proceedings of the 24th {C}hina National Conference on Computational Linguistics ({CCL} 2025)",
    month = aug,
    year = "2025",
    address = "Jinan, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://preview.aclanthology.org/port-ingestion-scripts/2025.ccl-2.45/",
    pages = "376--386",
    abstract = "``中医辨证辨病及中药处方生成评测任务专注于中医{``}辨证论治''。该任务由齐鲁工业大学(山东省科学院)与山东中医药大学附属医院联合发起,基于真实病历构建了中医{``}辨证论治{''}全流程公开数据集TCM-TBOSD,覆盖10类中医证型、4类中医疾病及381种常见中药。评测任务设立两个子任务:中医多标签辨证辨病与中药处方推荐,旨在系统评估大模型在中医诊疗全过程中的建模与推理能力。本次评测收到了学术界与产业界的广泛关注,评测共吸引123支队伍参与,35支队伍晋级复赛,最终提交了8份高质量技术报告。评测结果表明,大语言模型在中医任务中展现出良好的适应性与发展潜力,为中医智能化提供了可行路径与技术参考。详细信息可以从网址查看我们的评测任务。''"
}