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.

翻译：凭借大规模训练数据集和海量计算资源，大语言模型在综合能力和生成能力上取得了显著性能。基于这些强大的大语言模型，通过领域特定数据集微调的模型具备更专业的领域知识，因此更具实用性，例如医学大语言模型。然而，现有微调后的医学大语言模型局限于英语通用医学知识。对于疾病特定问题，模型的回答不够准确，有时甚至完全无关，尤其是在使用非英语语言时。本文聚焦于癫痫这一特定疾病，采用日语语言，引入定制的专用大语言模型EpilepsyLLM。该模型通过微调技术利用癫痫领域数据集从预训练大语言模型训练而来。数据集涵盖疾病基本信息、常见治疗方法与药物，以及生活和工作中的重要注意事项等知识。实验结果表明，EpilepsyLLM能够提供更可靠、更专业的医学知识回答。