EpilepsyLLM：基于癫痫医学知识微调的领域专用大语言模型
EpilepsyLLM: Domain-Specific Large Language Model Fine-tuned with Epilepsy Medical Knowledge
AI总结：
本文针对日语癫痫医学问答需求，提出基于癫痫领域数据集微调的专用大语言模型EpilepsyLLM，实验证明其能提供更可靠、更专业的医学知识响应。
摘要：
凭借大规模训练数据集和大量计算资源，大语言模型（LLMs）在综合能力与生成能力方面取得了显著性能。基于这些强大的大语言模型，使用领域特定数据集微调后的模型具备更专业的知识，因此更具实用性，例如医学大语言模型。然而，现有的微调医学大语言模型仅限于英语的通用医学知识。对于特定疾病问题，模型的响应不准确，有时甚至完全无关，尤其是在使用英语以外的语言时。在本工作中，我们聚焦于癫痫这一特定疾病并使用日语，提出了一种定制化的大语言模型，称为EpilepsyLLM。我们的模型通过在预训练大语言模型上使用癫痫领域的数据集进行微调训练得到。这些数据集包含疾病基本信息、常见治疗方法和药物，以及生活和工作中重要注意事项等知识。实验结果表明，EpilepsyLLM能够提供更可靠且更专业的医学知识响应。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
