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Leveraging Online Data to Enhance Medical Knowledge in a Small Persian Language Model

Authors :

Mehrdad Ghassabi¹ Pedram Rostami² Hamidreza Baradaran kashani³ Amirhossein Poursina⁴ Zahra Kazemi⁵ Milad Tavakoli⁶

1- دانشگاه اصفهان 2- دانشگاه تهران 3- دانشگاه اصفهان 4- دانشگاه علوم پزشکی اصفهان 5- دانشگاه اصفهان 6- دانشگاه اصفهان

Keywords :

persian medical question answering،small language model،medical language models،data crawling

Abstract :

The rapid advancement of language models has demonstrated the potential of artificial intelligence in the healthcare industry. However, small language models struggle with specialized domains in low-resource languages like Persian. While numerous medical-domain websites exist in Persian, no curated dataset or corpus has been available—making ours the first of its kind. This study explores the enhancement of medical knowledge in a small language model by leveraging accessible online data, including a crawled corpus from medical magazines and a dataset of real doctor-patient Q&A pairs. We fine-tuned a baseline model using our curated data to improve its medical knowledge.Benchmark evaluations demonstrate that the fine-tuned model achieves improved accuracy in medical question answering and provides better responses compared to its baseline. This work highlights the potential of leveraging open-access online data to enrich small language models in medical fields, providing a novel solution for Persian medical AI applications suitable for resource constrained environments.