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. Although 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 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 LLMs employ logical thinking during the task and provided insights and recommendations for future research.

中文翻译：

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基于知识增强语言模型的建设合同风险识别

合同审查是建设项目中防止潜在损失的重要步骤。然而，目前的建筑合同审查方法缺乏有效性和可靠性，导致流程耗时且容易出错。尽管大型语言模型 (LLM) 在彻底改变自然语言处理 (NLP) 任务方面表现出了希望，但它们在处理特定领域的知识和解决专门问题方面遇到了困难。本文提出了一种新颖的方法，利用具有建筑合同知识的法学硕士来模拟人类专家的合同审查过程。我们的免调整方法结合了施工合同领域知识，以增强用于识别施工合同风险的语言模型。构建领域知识库时使用自然语言有利于实际实施。我们在真实的建筑合同中评估了我们的方法并取得了良好的效果。此外，我们还调查了法学硕士在任务期间如何运用逻辑思维，并为未来的研究提供了见解和建议。