Fundamental research for adapting LLM to the civil engineering field

Abstract

The development of text generation models based on Large Language Models (LLMs), such as ChatGPT, has been remarkable. In the field of civil engineering, LLMs are also expected to improve work efficiency. However, since LLMs are mainly trained on documents collected from the Web, there is a concern that they may not be able to generate accurate text due to a lack of training on specialized knowledge in the field of civil engineering. Therefore, as a fundamental study to realize accurate text generation in the civil engineering field, this study attempted to adapt LLM to the civil engineering domain. We proposed an accuracy evaluation method, evaluated the accuracy of text generation in the civil engineering domain using a pre-trained public model of LLM and a model with fine tuning, and discussed the challenges in adapting LLM to the civil engineering domain.