  We construct an instruction dataset for the large language model (LLM) in the
Japanese finance domain. Domain adaptation of language models, including LLMs,
is receiving more attention as language models become more popular. This study
demonstrates the effectiveness of domain adaptation through instruction tuning.
To achieve this, we propose an instruction tuning data in Japanese called
JaFIn, the Japanese Financial Instruction Dataset. JaFIn is manually
constructed based on multiple data sources, including Japanese government
websites, which provide extensive financial knowledge. We then utilize JaFIn to
apply instruction tuning for several LLMs, demonstrating that our models
specialized in finance have better domain adaptability than the original
models. The financial-specialized LLMs created were evaluated using a
quantitative Japanese financial benchmark and qualitative response comparisons,
showing improved performance over the originals.
