Construction contract risk identification based on knowledge-augmented
  language model
Saika Wong Chunmo Zheng Xing Su Yinqiu Tang
Contract review is an essential step in construction projects to prevent
potential losses. However, the current methods for reviewing construction
contracts lack effectiveness and reliability, leading to time-consuming and
error-prone processes. While large language models (LLMs) have shown promise in
revolutionizing natural language processing (NLP) tasks, they struggle with
domain-specific knowledge and addressing specialized issues. This paper
presents a novel approach that leverages LLMs with construction contract
knowledge to emulate the process of contract review by human experts. Our
tuning-free approach incorporates construction contract domain knowledge to
enhance language models for identifying construction contract risks. The use of
a natural language when building the domain knowledge base facilitates
practical implementation. We evaluated our method on real construction
contracts and achieved solid performance. Additionally, we investigated how
large language models employ logical thinking during the task and provide
insights and recommendations for future research.