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

Abstract: The rapid advancement of LLMs has demonstrated the potential of artificial intelligence in the healthcare industry. However, small LLMs 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 LLM by leveraging accessible online data, including a crawled corpus from medical magazines and a dataset of real doctor-patient QA 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 LLMs in medical fields, providing a novel solution for Persian medical AI applications suitable for resource-constrained environments.