专为阿育吠陀医学打造的双语大模型，精准理解古典医典与临床知识。
AyurParam: A State-of-the-Art Bilingual Language Model for Ayurveda
- 基于Param-1-2.9B微调，融合英印双语阿育吠陀文本与问答数据
- 在BhashaBench-Ayur评测中超越同规模开源模型，媲美更大模型表现
- 适合医疗知识库构建、跨语言传统医学智能服务等场景
当前大语言模型在通用任务上表现优异，但在需要深厚文化、语言与专业知识的特定领域（如阿育吠陀医学）常表现不佳。阿育吠陀蕴含数百年来复杂的文本与临床知识，主流LLM难以准确解读或应用。我们提出AyurParam-2.9B，一个从Param-1-2.9B微调的领域专用双语模型，训练数据涵盖经典文献与临床指南，包含上下文感知、推理型及目标导向的英印双语问答，并采用严格的标注协议确保事实准确性与指令清晰性。在BhashaBench-Ayur基准测试中，AyurParam不仅超越同参数量级（1.5–3B）的所有开源指令微调模型，且性能可与更大模型竞争。结果表明，真实领域适配与高质量监督对实现可靠、文化契合的医学AI至关重要。
原文摘要 · Abstract (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.
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