Legal research is a crucial task in the practice of law. It requires intense human effort and intellectual prudence to research a legal case and prepare arguments. Recent boom in generative AI has not translated to proportionate rise in impactful legal applications, because of low trustworthiness and and the scarcity of specialized datasets for training Large Language Models (LLMs). This position paper explores the potential of LLMs within Legal Text Analytics (LTA), highlighting specific areas where the integration of human expertise can significantly enhance their performance to match that of experts. We introduce a novel dataset and describe a human centered, compound AI system that principally incorporates human inputs for performing LTA tasks with LLMs.

翻译：法律研究是法律实践中的一项关键任务，需要大量人力投入和审慎的智力工作来研究法律案件并准备论点。近年来生成式人工智能的蓬勃发展并未相应转化为有影响力的法律应用的显著增长，原因在于其可信度较低，且用于训练大型语言模型（LLMs）的专业数据集稀缺。本立场论文探讨了LLMs在法律文本分析（LTA）中的潜力，强调了通过整合人类专业知识可以显著提升其性能以达到专家水平的特定领域。我们引入了一个新颖的数据集，并描述了一个以人为本的复合人工智能系统，该系统的核心是在利用LLMs执行LTA任务时融入人工输入。