{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/leveraging-online-data-to-enhance-medical","title":"Leveraging Online Data to Enhance Medical Knowledge in a Small Persian Language Model","arxiv_id":"2505.16000","date":"2025-05-21","proceeding":null,"authors":["Mehrdad ghassabi","Pedram Rostami","Hamidreza Baradaran Kashani","Amirhossein Poursina","Zahra Kazemi","Milad Tavakoli"],"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 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 language models in medical fields, providing a novel solution for Persian medical AI applications suitable for resource-constrained environments.","url_abs":"https://arxiv.org/abs/2505.16000v1","url_pdf":"https://arxiv.org/pdf/2505.16000v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"leveraging-online-data-to-enhance-medical","repo_url":"https://github.com/mehrdadghassabi/gaokerena","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":null,"task_name":"Medical Question Answering"},{"task_slug":null,"task_name":"Patient QA"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"small-language-model","task_name":"Small Language Model"}],"methods":[],"datasets_introduced":[{"slug":"cpmc","name":"CPMC","full_name":"crawled persian medical corpus"},{"slug":"k-qa-fa","name":"K-QA(fa)","full_name":"persian translation of K-QA dataset"},{"slug":"mf3qa","name":"MF3QA","full_name":"Medical Free Form Farsi Question Answering dataset"},{"slug":"mf3qa-uncleaned","name":"MF3QA_uncleaned","full_name":"Medical Free Form Farsi Question Answering dataset (uncleaned)"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}