面向AI驱动演绎法律推理的软件工程方法
Software Engineering Methods For AI-Driven Deductive Legal Reasoning
- Carnegie Mellon University（卡内基梅隆大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文将LLMs视为自然语言程序解释器，从软件工程视角提出增强AI演绎法律推理的方法，并引入变异引导示例生成和蜕变测试等自动化元推理新应用。
AI中文摘要：
近年来，以预训练大语言模型（LLMs）为代表的生成式人工智能（AI）技术的迅速普及，为计算法学开辟了新的前沿。一个令人振奋的发展方向是利用AI自动化成文法和合同法中固有的基于规则的演绎推理。本文主张，此类自动化演绎法律推理现在可以从软件工程的视角加以审视，将LLMs视为以自然语言为输入的自然语言程序解释器。我们展示了如何运用有原则的软件工程技术来增强对复杂成文法的AI驱动法律推理，并解锁自动化元推理中的新应用，例如变异引导的示例生成和基于蜕变属性的测试。
英文摘要：
The recent proliferation of generative artificial intelligence (AI) technologies such as pre-trained large language models (LLMs) has opened up new frontiers in computational law. An exciting area of development is the use of AI to automate the deductive rule-based reasoning inherent in statutory and contract law. This paper argues that such automated deductive legal reasoning can now be viewed from the lens of software engineering, treating LLMs as interpreters of natural-language programs with natural-language inputs. We show how it is possible to apply principled software engineering techniques to enhance AI-driven legal reasoning of complex statutes and to unlock new applications in automated meta-reasoning such as mutation-guided example generation and metamorphic property-based testing.
