The development of Large Language Models (LLMs) in various languages has been advancing, but the combination of non-English languages with domain-specific contexts remains underexplored. This paper presents our findings from training and evaluating a Japanese business domain-specific LLM designed to better understand business-related documents, such as the news on current affairs, technical reports, and patents. Additionally, LLMs in this domain require regular updates to incorporate the most recent knowledge. Therefore, we also report our findings from the first experiments and evaluations involving updates to this LLM using the latest article data, which is an important problem setting that has not been addressed in previous research. From our experiments on a newly created benchmark dataset for question answering in the target domain, we found that (1) our pretrained model improves QA accuracy without losing general knowledge, and (2) a proper mixture of the latest and older texts in the training data for the update is necessary. Our pretrained model and business domain benchmark are publicly available to support further studies.

翻译：多语言大语言模型的发展持续推进，但非英语语言与领域特定情境的结合仍待深入探索。本文展示了我们在训练与评估日本商业领域特定大语言模型方面的研究成果，该模型旨在更好地理解商业相关文档，如时事新闻、技术报告与专利。此外，该领域的大语言模型需要定期更新以纳入最新知识。因此，我们亦报告了利用最新文章数据对该模型进行更新的首次实验与评估结果，这是先前研究中尚未涉及的重要问题设定。通过对新构建的目标领域问答基准数据集的实验，我们发现：（1）我们的预训练模型在保持通用知识的同时提升了问答准确率；（2）更新训练数据中最新文本与历史文本的适当混合至关重要。我们的预训练模型与商业领域基准数据集已公开提供，以支持后续研究。