2024/07/16 by Qimin Yang, Rongsheng Wang, Yang, Qimin +7 · 1 citation
Computer Science · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning in Healthcare #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2407.11536
openalex publication_date 2024/07/16 · openalex created_date 2024/10/26 · openalex updated_date 2026/07/28
Large Language Models (LLMs) have been widely applied in various professional fields. By fine-tuning the models using domain specific question and answer datasets, the professional domain knowledge and Q&A abilities of these models have significantly improved, for example, medical professional LLMs that use fine-tuning of doctor-patient Q&A data exhibit extraordinary disease diagnostic abilities. However, we observed that despite improvements in specific domain knowledge, the performance of medical LLM in long-context understanding has significantly declined, especially compared to general language models with similar parameters. The purpose of this study is to investigate the phenomenon of reduced performance in understanding long-context in medical LLM. We designed a series of experiments to conduct open-book professional knowledge exams on all models to evaluate their ability to read long-context. By adjusting the proportion and quantity of general data and medical data in the process of fine-tuning, we can determine the best data composition to optimize the professional model and achieve a balance between long-context performance and specific domain knowledge.