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

Mohd Nauman; Sravan Gvm; Vijay Devane; Shyam Pawar; Viraj Thakur; Kundeshwar Pundalik; Piyush Sawarkar; Rohit Saluja; Maunendra Desarkar; Ganesh Ramakrishnan

arXiv · 2511.02374

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

Mohd Nauman·Sravan Gvm·Vijay Devane·Shyam Pawar·Viraj Thakur·Kundeshwar Pundalik·Piyush Sawarkar·Rohit Saluja·Maunendra Desarkar·Ganesh Ramakrishnan

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Abstract

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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Mohd Nauman, Sravan Gvm, Vijay Devane, Shyam Pawar, Viraj Thakur, Kundeshwar Pundalik, Piyush Sawarkar, Rohit Saluja, Maunendra Desarkar, Ganesh Ramakrishnan. 2025-11-04. AyurParam: A State-of-the-Art Bilingual Language Model for Ayurveda. https://arxiv.org/abs/2511.02374

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