Instruction tuning is essential for large language models (LLMs) to become interactive. While many instruction tuning datasets exist in English, there is a noticeable lack in other languages. Also, their effectiveness has not been well verified in non-English languages. We construct a Japanese instruction dataset by expanding and filtering existing datasets and apply the dataset to a Japanese pre-trained base model. We performed Low-Rank Adaptation (LoRA) tuning on both Japanese and English existing models using our instruction dataset. We evaluated these models from both quantitative and qualitative perspectives. As a result, the effectiveness of Japanese instruction datasets is confirmed. The results also indicate that even with relatively small LLMs, performances in downstream tasks would be improved through instruction tuning. Our instruction dataset, tuned models, and implementation are publicly available online.

翻译：指令调优对于大语言模型实现交互功能至关重要。尽管英语中已存在大量指令调优数据集，但其他语言在此领域明显匮乏。此外，指令调优在非英语语言中的有效性尚未得到充分验证。我们通过扩展和筛选现有数据集构建了日语指令数据集，并将其应用于日语预训练基础模型。我们利用该指令数据集对现有日语和英语模型执行了低秩适配（LoRA）调优，并从定量与定性两个角度对模型进行了评估。结果证实了日语指令数据集的有效性，同时表明即使对于规模较小的大语言模型，指令调优也能提升下游任务性能。我们的指令数据集、调优模型及实现均已公开在线发布。