Legal Judgment Prediction (LJP) has become an increasingly crucial task in Legal AI, i.e., predicting the judgment of the case in terms of case fact description. Precedents are the previous legal cases with similar facts, which are the basis for the judgment of the subsequent case in national legal systems. Thus, it is worthwhile to explore the utilization of precedents in the LJP. Recent advances in deep learning have enabled a variety of techniques to be used to solve the LJP task. These can be broken down into two categories: large language models (LLMs) and domain-specific models. LLMs are capable of interpreting and generating complex natural language, while domain models are efficient in learning task-specific information. In this paper, we propose the precedent-enhanced LJP framework (PLJP), a system that leverages the strength of both LLM and domain models in the context of precedents. Specifically, the domain models are designed to provide candidate labels and find the proper precedents efficiently, and the large models will make the final prediction with an in-context precedents comprehension. Experiments on the real-world dataset demonstrate the effectiveness of our PLJP. Moreover, our work shows a promising direction for LLM and domain-model collaboration that can be generalized to other vertical domains.

翻译：法律判决预测（LJP）已成为法律人工智能中日益关键的任务，即根据案件事实描述预测判决结果。先例是指具有相似事实的既往法律案件，在大陆法系和英美法系中均为后续案件判决的依据。因此，探索先例在LJP中的应用具有重要意义。深度学习的最新进展催生了多种解决LJP任务的技术，这些技术可分为两类：大语言模型（LLM）和领域专用模型。大语言模型擅长理解和生成复杂自然语言，而领域模型则在学习任务特定信息方面具有高效性。本文提出基于先例增强的LJP框架（PLJP），该系统在先例语境中融合了大语言模型与领域模型的双重优势。具体而言，领域模型负责高效提供候选标签并检索相关先例，大语言模型则通过上下文先例理解做出最终判决。基于真实数据集的实验验证了PLJP的有效性。此外，本研究展示了大语言模型与领域模型协作的可行方向，该范式可推广至其他垂直领域。