Since the rise of large language models (LLMs), the domain adaptation has
been one of the hot topics in various domains. Many medical LLMs trained with
English medical dataset have made public recently. However, Japanese LLMs in
medical domain still lack its research. Here we utilize multiple 70B-parameter
LLMs for the first time and show that instruction tuning using Japanese medical
question-answering dataset significantly improves the ability of Japanese LLMs
to solve Japanese medical license exams, surpassing 50\% in accuracy. In
particular, the Japanese-centric models exhibit a more significant leap in
improvement through instruction tuning compared to their English-centric
counterparts. This underscores the importance of continual pretraining and the
adjustment of the tokenizer in our local language. We also examine two slightly
different prompt formats, resulting in non-negligible performance improvement.

通过使用多个 70B 参数的大型语言模型以及日本医学问答数据集进行指导调整，我们首次展示了指导调整显著提高了日本医学领域的语言模型在解决日本医学许可考试方面的准确性，超过了 50%。特别是，与英文为中心的模型相比，以日语为中心的模型在通过指导调整方面取得了更大的改进，这凸显了我们地方语言的持续预训练和标记工具的调整的重要性。我们还检验了两种略有不同的提示格式，结果显示有可观的性能改进。