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English(EN) Lingdan-V2: Large language models for clinically grounded reasoning in traditional Chinese medicine

新LLM专为中医临床辨证开发

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一系列大型语言模型，参数量从40亿到140亿不等，专门用于中医药（TCM）临床辨证。这些模型采用了一种新颖的分阶段临床对齐框架，该框架结合了中医药数据的持续预训练、互补推理监督以及以处方为中心的强化学习。在强化学习后，参数量为140亿的模型在一个公开基准上达到了37.0的处方生成F1分数，在一个私有临床数据集上达到了52.8的草药级别F1分数，在某些指标上优于更大的DeepSeek-R1模型。 AI

影响 这些模型可以推动人工智能在专业医学领域的应用，为传统医学实践提供临床决策支持。

排序理由 该集群描述了一篇研究论文，详细介绍了为特定领域开发新LLM的情况。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新LLM专为中医临床辨证开发

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该集群描述了一篇研究论文，详细介绍了为特定领域开发新LLM的情况。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · 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 ·

    Lingdan-V2：用于中医临床推理的大型语言模型

    arXiv:2610.14705v1 Announce Type: cross 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 diffe…

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