Construction of Domain-specified Japanese Large Language Model for Finance through Continual Pre-training
This work addresses the need for domain-specific language models in Japanese finance, though it is incremental as it applies existing continual pre-training methods to a new domain.
The study tackled the lack of Japanese financial-specific large language models by constructing one through continual pre-training, resulting in a model that outperformed the original on Japanese financial benchmarks with improved answer quality and length.
Large language models (LLMs) are now widely used in various fields, including finance. However, Japanese financial-specific LLMs have not been proposed yet. Hence, this study aims to construct a Japanese financial-specific LLM through continual pre-training. Before tuning, we constructed Japanese financial-focused datasets for continual pre-training. As a base model, we employed a Japanese LLM that achieved state-of-the-art performance on Japanese financial benchmarks among the 10-billion-class parameter models. After continual pre-training using the datasets and the base model, the tuned model performed better than the original model on the Japanese financial benchmarks. Moreover, the outputs comparison results reveal that the tuned model's outputs tend to be better than the original model's outputs in terms of the quality and length of the answers. These findings indicate that domain-specific continual pre-training is also effective for LLMs. The tuned model is publicly available on Hugging Face.