  With large training datasets and massive amounts of computing sources, large
language models (LLMs) achieve remarkable performance in comprehensive and
generative ability. Based on those powerful LLMs, the model fine-tuned with
domain-specific datasets posseses more specialized knowledge and thus is more
practical like medical LLMs. However, the existing fine-tuned medical LLMs are
limited to general medical knowledge with English language. For
disease-specific problems, the model's response is inaccurate and sometimes
even completely irrelevant, especially when using a language other than
English. In this work, we focus on the particular disease of Epilepsy with
Japanese language and introduce a customized LLM termed as EpilepsyLLM. Our
model is trained from the pre-trained LLM by fine-tuning technique using
datasets from the epilepsy domain. The datasets contain knowledge of basic
information about disease, common treatment methods and drugs, and important
notes in life and work. The experimental results demonstrate that EpilepsyLLM
can provide more reliable and specialized medical knowledge responses.
