CCL25-Eval任务9系统报告:一种面向中医辨证与处方生成任务的检索增强大模型方法

Yiyang Kang; Yao Jiaqi; Tengxiao Lv; Bo Xu; Ling Luo (罗凌); Yuanyuan Sun (孙媛媛, 孙嫒媛); Hongfei Lin (林鸿飞)

CCL25-Eval任务9系统报告:一种面向中医辨证与处方生成任务的检索增强大模型方法

Yiyang Kang, Yao Jiaqi, Tengxiao Lv, Bo Xu, Ling Luo, Yuanyuan Sun, Hongfei Lin

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"本文面向CCL2025-Eval任务9中的中医辨证辨病与中药处方推荐两个子任务,提出了一套基于大语言模型的系统性方法。在子任务1中,本文基于QLoRA方法对Qwen2.5-7B、Mistral-7B和Baichuan-7B三种预训练模型进行高效微调,并引入多模型集成投票策略。在子任务串中,本文设计了融合向量检索、监督微调与强化学习的中药推荐框架,通过相似度检索构建候选处方集合,并利用强化学习优化模型的生成能力。最终在评测中获得总分0.5171(Task1得分0.5710,Task2得分0.4632),排名第四,验证了所提方法的有效性与实用性。"

Anthology ID:: 2025.ccl-2.42
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:: 355–362
Language:
URL:: https://aclanthology.org/2025.ccl-2.42/
DOI:
Bibkey:
Cite (ACL):: Yiyang Kang, Yao Jiaqi, Tengxiao Lv, Bo Xu, Ling Luo, Yuanyuan Sun, and Hongfei Lin. 2025. CCL25-Eval任务9系统报告:一种面向中医辨证与处方生成任务的检索增强大模型方法. In Proceedings of the 24th China National Conference on Computational Linguistics (CCL 2025), pages 355–362, Jinan, China. Chinese Information Processing Society of China.
Cite (Informal):: CCL25-Eval任务9系统报告:一种面向中医辨证与处方生成任务的检索增强大模型方法 (Kang et al., CCL 2025)
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PDF:: https://aclanthology.org/2025.ccl-2.42.pdf

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@inproceedings{kang-etal-2025-ccl25,
    title = "{CCL}25-Eval任务9系统报告:一种面向中医辨证与处方生成任务的检索增强大模型方法",
    author = "Kang, Yiyang  and
      Jiaqi, Yao  and
      Lv, Tengxiao  and
      Xu, Bo  and
      Luo, Ling  and
      Sun, Yuanyuan  and
      Lin, Hongfei",
    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://aclanthology.org/2025.ccl-2.42/",
    pages = "355--362",
    abstract = "``本文面向CCL2025-Eval任务9中的中医辨证辨病与中药处方推荐两个子任务,提出了一套基于大语言模型的系统性方法。在子任务1中,本文基于QLoRA方法对Qwen2.5-7B、Mistral-7B和Baichuan-7B三种预训练模型进行高效微调,并引入多模型集成投票策略。在子任务串中,本文设计了融合向量检索、监督微调与强化学习的中药推荐框架,通过相似度检索构建候选处方集合,并利用强化学习优化模型的生成能力。最终在评测中获得总分0.5171(Task1得分0.5710,Task2得分0.4632),排名第四,验证了所提方法的有效性与实用性。''"
}

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%0 Conference Proceedings
%T CCL25-Eval任务9系统报告:一种面向中医辨证与处方生成任务的检索增强大模型方法
%A Kang, Yiyang
%A Jiaqi, Yao
%A Lv, Tengxiao
%A Xu, Bo
%A Luo, Ling
%A Sun, Yuanyuan
%A Lin, Hongfei
%Y Lin, Hongfei
%Y Li, Bin
%Y Tan, Hongye
%S Proceedings of the 24th China National Conference on Computational Linguistics (CCL 2025)
%D 2025
%8 August
%I Chinese Information Processing Society of China
%C Jinan, China
%F kang-etal-2025-ccl25
%X “本文面向CCL2025-Eval任务9中的中医辨证辨病与中药处方推荐两个子任务,提出了一套基于大语言模型的系统性方法。在子任务1中,本文基于QLoRA方法对Qwen2.5-7B、Mistral-7B和Baichuan-7B三种预训练模型进行高效微调,并引入多模型集成投票策略。在子任务串中,本文设计了融合向量检索、监督微调与强化学习的中药推荐框架,通过相似度检索构建候选处方集合,并利用强化学习优化模型的生成能力。最终在评测中获得总分0.5171(Task1得分0.5710,Task2得分0.4632),排名第四,验证了所提方法的有效性与实用性。”
%U https://aclanthology.org/2025.ccl-2.42/
%P 355-362

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Markdown (Informal)

[CCL25-Eval任务9系统报告:一种面向中医辨证与处方生成任务的检索增强大模型方法](https://aclanthology.org/2025.ccl-2.42/) (Kang et al., CCL 2025)

CCL25-Eval任务9系统报告:一种面向中医辨证与处方生成任务的检索增强大模型方法 (Kang et al., CCL 2025)

ACL

Yiyang Kang, Yao Jiaqi, Tengxiao Lv, Bo Xu, Ling Luo, Yuanyuan Sun, and Hongfei Lin. 2025. CCL25-Eval任务9系统报告:一种面向中医辨证与处方生成任务的检索增强大模型方法. In Proceedings of the 24th China National Conference on Computational Linguistics (CCL 2025), pages 355–362, Jinan, China. Chinese Information Processing Society of China.