  Several previous studies have considered language- and domain-specific large
language models (LLMs) as separate topics. This study explores the combination
of a non-English language and a high-demand industry domain, focusing on a
Japanese business-specific LLM. This type of a model requires expertise in the
business domain, strong language skills, and regular updates of its knowledge.
We trained a 13-billion-parameter LLM from scratch using a new dataset of
business texts and patents, and continually pretrained it with the latest
business documents. Further we propose a new benchmark for Japanese business
domain question answering (QA) and evaluate our models on it. The results show
that our pretrained model improves QA accuracy without losing general
knowledge, and that continual pretraining enhances adaptation to new
information. Our pretrained model and business domain benchmark are publicly
available.
