AyurParam：面向阿育吠陀医学的尖端双语语言模型

摘要

当前的大语言模型在通用任务上表现卓越，但在需要深厚文化、语言及专业知识的垂直领域始终存在性能短板。以阿育吠陀为代表的传统医学体系蕴含数百年来精微的文本与临床知识，主流大语言模型难以准确解读或应用这些专业内容。我们推出AyurParam-2.9B——基于Param-1-2.9B微调的专业领域双语模型，其训练数据覆盖阿育吠陀经典典籍与临床指导的大规模专家标注数据集。该数据集融合语境感知、推理思维及客观题型问答（含英语与印地语双版本），并通过严谨的标注流程确保事实精确性与指导清晰度。在BhashaBench-Ayur基准测试中，AyurParam不仅优于同参数规模（1.5-30亿）的所有开源指令微调模型，更在多项指标上超越参数量更大的模型。这一成果印证了专业领域人工智能实现可靠性与文化适配性必需的两个核心要素：真正的领域适应与高质量监督机制。

English

Current large language models excel at broad, general-purpose tasks, but consistently underperform when exposed to highly specialized domains that require deep cultural, linguistic, and subject-matter expertise. In particular, traditional medical systems such as Ayurveda embody centuries of nuanced textual and clinical knowledge that mainstream LLMs fail to accurately interpret or apply. We introduce AyurParam-2.9B, a domain-specialized, bilingual language model fine-tuned from Param-1-2.9B using an extensive, expertly curated Ayurveda dataset spanning classical texts and clinical guidance. AyurParam's dataset incorporates context-aware, reasoning, and objective-style Q&A in both English and Hindi, with rigorous annotation protocols for factual precision and instructional clarity. Benchmarked on BhashaBench-Ayur, AyurParam not only surpasses all open-source instruction-tuned models in its size class (1.5--3B parameters), but also demonstrates competitive or superior performance compared to much larger models. The results from AyurParam highlight the necessity for authentic domain adaptation and high-quality supervision in delivering reliable, culturally congruent AI for specialized medical knowledge.

AyurParam：面向阿育吠陀医学的尖端双语语言模型

AyurParam: A State-of-the-Art Bilingual Language Model for Ayurveda

摘要

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