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.

我们构建了一个日本指令数据集，并将其应用于一个日本预训练基础模型。通过我们的指令数据集，对日本和英文现有模型进行了低秩调整（LoRA）。从定量和定性的角度评估了这些模型，结果证实了日本指令数据集的有效性。同时也指出，即使在相对较小的大语言模型中，通过指令调整也能提高下游任务的性能。我们的指令数据集、调整模型和实现代码已在网上公开提供。

从基础到会话：日语指导数据集和调整大型语言模型