Application of large language models to intelligently analyze long construction contract texts

The emergence of large language models (LLMs) has provided an opportunity to assist humans in quickly reading, searching, and understanding the contents of construction contracts. However, the limited context length of LLMs restricts their ability to process lengthy contract documents, which hinders their application in the construction industry. This study proposes intelligent analysis methods for long construction contracts, which enables LLMs to handle contracts that exceed their context length through a two-stage text segmentation. We further utilized the segmented text chunks for content compression and intelligent question-answering applications. The FIDIC contract was used for testing. A condensed version of lengthy contracts summarizes the provisions in a shorter format. It maintains a high level of correctness and readability, offering practitioners additional options to read contracts of varying lengths based on their needs. The satisfaction rate of the question-answering outcomes reached 93.3%, allowing practitioners to quickly obtain specific clauses of interest and relevant contract knowledge through personalized queries. The performance of the 8K model using our methods is comparable to that of the 128K long-text models, while reducing computational power. This study expands the potential for applications of LLMs in contract management within the construction industry.