arXiv：cs.AI· Rui Hua, Yu Wei, Ziri Jin, Zhuo Sun, Kai Chang, Jianan Xia, Zixin Shu, Xiaodong Li, Dongmei Jia, Fei Dong, Runshun Zhang, Jian Yu, Hao Xu, Haibin Yu, Jiansheng Li, Baoyan Liu, Wenjia Wang, Xuezhong Zhou·· 16 小时前
Lingdan-V2：面向中医临床推理的大语言模型
Lingdan-V2: Large language models for clinically grounded reasoning in traditional Chinese medicine
AI 导读
Lingdan-V2 中医语言模型家族包含 4B、8B、14B 三种参数规模。14B 强化对齐模型在含 25,620 道专家验证题目的公开基准上取得处方生成 F1 37.0，高于 671B 总参数的 DeepSeek-R1 的 32.3，并在 4,348 例脾胃病处方回顾数据上取得药材级 F1 52.8，剂量一致性亦有提升。
正文
Authors:Rui Hua, Yu Wei, Ziri Jin, Zhuo Sun, Kai Chang, Jianan Xia, Zixin Shu, Xiaodong Li, Dongmei Jia, Fei Dong, Runshun Zhang, Jian Yu, Hao Xu, Haibin Yu, Jiansheng Li, Baoyan Liu, Wenjia Wang, Xuezhong Zhou
Abstract:Large language models have advanced medical reasoning, but clinically grounded reasoning in traditional Chinese medicine (TCM) remains challenging because prescription decisions require integrating domain knowledge, syndrome differentiation, treatment principles, herbal prescription composition, and dosage determination. We develop a TCM language model family comprising models with 4 billion, 8 billion, and 14 billion parameters. The artificial intelligence contribution is a staged clinical alignment framework integrating large scale continued pretraining on TCM and clinical records, complementary reasoning supervision, and prescription-centered reinforcement learning (RL). We further formulate herbal prescription generation as a structured RL problem with prescription-level feedback, enabling direct optimization of clinical decision outputs beyond knowledge reproduction and generic instruction following. The engineering application is TCM clinical decision support, particularly herbal prescription recommendation. We evaluate the models on a public benchmark containing 25,620 expert-verified items across 13 TCM knowledge and clinical reasoning tasks and on a private retrospective dataset of 4,348 spleen and stomach disease prescription cases. The 14 billion parameter reinforcement aligned model achieves a prescription generation F1 of 37.0 on the public benchmark, compared with 32.3 for DeepSeek-R1 with 671 billion total parameters, and a herb-level F1 of 52.8 on the clinical dataset with improved dosage agreement. Reinforcement post-training consistently improves prescription recommendation across all scales, while physician-source analysis shows that expert heterogeneity affects reward stability. These findings demonstrate a scalable pathway from domain grounding and reasoning supervision to clinically oriented prescription decision support.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.14705 [cs.CL] |
| (or arXiv:2610.14705v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.14705 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Rui Hua [view email]
[v1]
Sat, 10 Oct 2026 08:22:12 UTC (1,154 KB)
来源：arXiv：cs.AI · arxiv.org