Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.02374 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912686945599488 |
|---|---|
| author | Nauman, Mohd Gvm, Sravan Devane, Vijay Pawar, Shyam Thakur, Viraj Pundalik, Kundeshwar Sawarkar, Piyush Saluja, Rohit Desarkar, Maunendra Ramakrishnan, Ganesh |
| author_facet | Nauman, Mohd Gvm, Sravan Devane, Vijay Pawar, Shyam Thakur, Viraj Pundalik, Kundeshwar Sawarkar, Piyush Saluja, Rohit Desarkar, Maunendra Ramakrishnan, Ganesh |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_02374 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AyurParam: A State-of-the-Art Bilingual Language Model for Ayurveda Nauman, Mohd Gvm, Sravan Devane, Vijay Pawar, Shyam Thakur, Viraj Pundalik, Kundeshwar Sawarkar, Piyush Saluja, Rohit Desarkar, Maunendra Ramakrishnan, Ganesh Computation and Language Artificial Intelligence 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. |
| title | AyurParam: A State-of-the-Art Bilingual Language Model for Ayurveda |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2511.02374 |