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。"
- Anthology ID:
- 2025.ccl-2.45
- Volume:
- Proceedings of the 24th China National Conference on Computational Linguistics (CCL 2025)
- Month:
- August
- Year:
- 2025
- Address:
- Jinan, China
- Editors:
- Hongfei Lin, Bin Li, Hongye Tan
- Venue:
- CCL
- SIG:
- Publisher:
- Chinese Information Processing Society of China
- Note:
- Pages:
- 376–386
- Language:
- URL:
- https://aclanthology.org/2025.ccl-2.45/
- DOI:
- Bibkey:
- Cite (ACL):
- Cong Wang, Zhizhuo Zhao, Yishuo Li, Hongjiao Guan, Yifei Wang, Zhenyu Li, and Wenpeng Lu. 2025. CCL25-Eval任务9总结报告:中医辨证辨病及中药处方生成评测. In Proceedings of the 24th China National Conference on Computational Linguistics (CCL 2025), pages 376–386, Jinan, China. Chinese Information Processing Society of China.
- Cite (Informal):
- CCL25-Eval任务9总结报告:中医辨证辨病及中药处方生成评测 (Wang et al., CCL 2025)
- Copy Citation:
- PDF:
- https://aclanthology.org/2025.ccl-2.45.pdf