通过 ChatGPT 从合同中提取规范：机遇与挑战
Extracting Norms from Contracts Via ChatGPT: Opportunities and Challenges
AI总结：
本文评估 ChatGPT 从合同中提取承诺、禁止、授权、权力及相关要素的能力，验证其零样本潜力并指出导致错误的主要局限。
摘要：
我们研究了 ChatGPT 在从合同中提取规范方面的有效性。规范通过刻画如何治理两个或多个自治方之间的交互，为工程化多智能体系统提供了一种自然方式。我们从合同中提取承诺、禁止、授权和权力等规范，以及相关规范要素（所涉各方、前件和后件）。我们的调查揭示了 ChatGPT 在从合同中提取规范时的有效性与局限性。ChatGPT 在无需训练或微调的情况下表现出颇具前景的规范提取性能，从而消除了对标注数据的需求，而此类数据在该领域通常并不可得。然而，我们发现 ChatGPT 在提取这些规范时存在一些会导致错误规范提取的局限性。这些局限包括忽略关键细节、幻觉、对连接词解析错误，以及规范要素为空。增强从合同中提取规范的能力，可促进更透明、可信的形式化智能体交互规范的发展，从而助力多智能体系统的改进。
英文摘要：
We investigate the effectiveness of ChatGPT in extracting norms from contracts. Norms provide a natural way to engineer multiagent systems by capturing how to govern the interactions between two or more autonomous parties. We extract norms of commitment, prohibition, authorization, and power, along with associated norm elements (the parties involved, antecedents, and consequents) from contracts. Our investigation reveals ChatGPT's effectiveness and limitations in norm extraction from contracts. ChatGPT demonstrates promising performance in norm extraction without requiring training or fine-tuning, thus obviating the need for annotated data, which is not generally available in this domain. However, we found some limitations of ChatGPT in extracting these norms that lead to incorrect norm extractions. The limitations include oversight of crucial details, hallucination, incorrect parsing of conjunctions, and empty norm elements. Enhanced norm extraction from contracts can foster the development of more transparent and trustworthy formal agent interaction specifications, thereby contributing to the improvement of multiagent systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
