This study constructed a Japanese chat dataset for tuning large language
models (LLMs), which consist of about 8.4 million records. Recently, LLMs have
been developed and gaining popularity. However, high-performing LLMs are
usually mainly for English. There are two ways to support languages other than
English by those LLMs: constructing LLMs from scratch or tuning existing
models. However, in both ways, datasets are necessary parts. In this study, we
focused on supporting Japanese in those LLMs and making a dataset for training
or tuning LLMs in Japanese. The dataset we constructed consisted of various
tasks, such as translation and knowledge tasks. In our experiment, we tuned an
existing LLM using our dataset and evaluated the performance qualitatively. The
results suggest that our dataset is possibly beneficial for LLMs. However, we
also revealed some difficulties in constructing LLMs in languages other than
English.

该研究构建了一个日语聊天数据集，用于调整大型语言模型（LLMs），结果显示该数据集有可能对 LLMs 有利，但也揭示了在非英语语言中构建 LLMs 所面临的一些困难。