Construction contract risk identification based on knowledge-augmented language model
This addresses the time-consuming and error-prone contract review process in construction projects, though it appears incremental by adapting existing methods to a specialized domain.
This paper tackled the problem of ineffective and unreliable construction contract review by developing a tuning-free approach that enhances large language models with domain-specific knowledge to identify risks, achieving solid performance on real contracts.
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.