专为中医设计的大型语言模型，提升辨证诊断能力。
BianCang: A Traditional Chinese Medicine Large Language Model
- 分两阶段训练：先注入中医知识，再通过真实病历对齐优化
- 在11个测试集上优于31个模型，4项任务表现领先
- 适合中医AI研究者、临床辅助诊断系统开发者使用
大语言模型的兴起推动了医学领域的进步，包括中医药。然而，现有医学大模型在中医诊断和证候辨识方面仍存在瓶颈，主要因中西医理论差异大，且高质量专业语料稀缺。为此，本文提出针对中医的专用大模型BianCang，采用两阶段训练策略：首先注入领域知识，再通过针对性刺激对齐，强化诊断与辨识能力。具体构建了基于真实医院记录的指令对齐数据集、源自《中华人民共和国药典》的ChP-TCM数据集，以及涵盖广泛中医与现代医学语料的持续预训练与监督微调数据集，全面增强模型对中医的理解。在涉及31个模型、4项任务的11个测试集上评估表明，BianCang效果显著，为未来研究提供重要参考。代码、数据集与模型已开源至https://github.com/QLU-NLP/BianCang。
原文摘要 · Abstract (English)
The surge of large language models (LLMs) has driven significant progress in medical applications, including traditional Chinese medicine (TCM). However, current medical LLMs struggle with TCM diagnosis and syndrome differentiation due to substantial differences between TCM and modern medical theory, and the scarcity of specialized, high-quality corpora. To this end, in this paper we propose BianCang, a TCM-specific LLM, using a two-stage training process that first injects domain-specific knowledge and then aligns it through targeted stimulation to enhance diagnostic and differentiation capabilities. Specifically, we constructed pre-training corpora, instruction-aligned datasets based on real hospital records, and the ChP-TCM dataset derived from the Pharmacopoeia of the People's Republic of China. We compiled extensive TCM and medical corpora for continual pre-training and supervised fine-tuning, building a comprehensive dataset to refine the model's understanding of TCM. Evaluations across 11 test sets involving 31 models and 4 tasks demonstrate the effectiveness of BianCang, offering valuable insights for future research. Code, datasets, and models are available on https://github.com/QLU-NLP/BianCang.
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