Interpreting the meaning of legal open-textured terms is a key task of legal professionals. An important source for this interpretation is how the term was applied in previous court cases. In this paper, we evaluate the performance of GPT-4 in generating factually accurate, clear and relevant explanations of terms in legislation. We compare the performance of a baseline setup, where GPT-4 is directly asked to explain a legal term, to an augmented approach, where a legal information retrieval module is used to provide relevant context to the model, in the form of sentences from case law. We found that the direct application of GPT-4 yields explanations that appear to be of very high quality on their surface. However, detailed analysis uncovered limitations in terms of the factual accuracy of the explanations. Further, we found that the augmentation leads to improved quality, and appears to eliminate the issue of hallucination, where models invent incorrect statements. These findings open the door to the building of systems that can autonomously retrieve relevant sentences from case law and condense them into a useful explanation for legal scholars, educators or practicing lawyers alike.

翻译：解释法律开放性文本术语的含义是法律专业人士的关键任务。这一解释的重要来源是术语在先前法院案例中的应用方式。本文评估了GPT-4在生成立法术语解释时的准确性、清晰度及相关性表现。我们比较了基线设置（直接要求GPT-4解释法律术语）与增强方法（通过法律信息检索模块为模型提供相关上下文，即判例法中的句子）的性能。研究发现，直接应用GPT-4生成的解释表面上质量极高，但详细分析揭示了其在事实准确性方面的局限性。此外，增强方法显著提升了质量，似乎消除了模型编造错误陈述的幻觉问题。这些发现为构建能够自主检索判例法相关句子并凝练成对法律学者、教育者或执业律师有用的解释系统开辟了道路。