  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.
