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.

翻译：合同审查是建筑工程项目中防止潜在损失的重要环节。然而，当前建筑工程合同的审查方法缺乏有效性和可靠性，导致耗时长且易出错。尽管大语言模型（LLMs）在革新自然语言处理（NLP）任务方面展现出潜力，但它们仍难以应对领域特定知识和处理专业问题。本文提出了一种新颖方法，将大语言模型与建筑工程合同知识相结合，以模拟人类专家审查合同的过程。我们的免调优方法融合了建筑工程合同领域知识，以增强语言模型识别建筑工程合同风险的能力。在构建领域知识库时使用自然语言，便于实际应用。我们在真实建筑工程合同上对该方法进行了评估，取得了稳定的性能表现。此外，我们还探究了大语言模型在执行任务时运用逻辑思维的方式，并为未来研究提供了见解与建议。