In this paper, we address the issue of using logic rules to explain the results from legal case retrieval. The task is critical to legal case retrieval because the users (e.g., lawyers or judges) are highly specialized and require the system to provide logical, faithful, and interpretable explanations before making legal decisions. Recently, research efforts have been made to learn explainable legal case retrieval models. However, these methods usually select rationales (key sentences) from the legal cases as explanations, failing to provide faithful and logically correct explanations. In this paper, we propose Neural-Symbolic enhanced Legal Case Retrieval (NS-LCR), a framework that explicitly conducts reasoning on the matching of legal cases through learning case-level and law-level logic rules. The learned rules are then integrated into the retrieval process in a neuro-symbolic manner. Benefiting from the logic and interpretable nature of the logic rules, NS-LCR is equipped with built-in faithful explainability. We also show that NS-LCR is a model-agnostic framework that can be plugged in for multiple legal retrieval models. To showcase NS-LCR's superiority, we enhance existing benchmarks by adding manually annotated logic rules and introducing a novel explainability metric using Large Language Models (LLMs). Our comprehensive experiments reveal NS-LCR's effectiveness for ranking, alongside its proficiency in delivering reliable explanations for legal case retrieval.

翻译：本文研究了利用逻辑规则解释法律案例检索结果的问题。该任务对法律案例检索至关重要，因为用户（如律师或法官）具有高度专业性，需要在做出法律决策前由系统提供逻辑严谨、可信且可解释的解释。近年来，学界致力于学习可解释的法律案例检索模型，但现有方法通常从法律案例中选取关键句（rationales）作为解释，未能提供忠实且逻辑正确的解释。为此，我们提出神经符号增强的法律案例检索（NS-LCR）框架，该框架通过学习案例级和法律级逻辑规则，明确推演法律案例的匹配关系，并将学习到的规则以神经符号方式融入检索过程。得益于逻辑规则本身的逻辑性与可解释性，NS-LCR具备内置的可信可解释性。我们还证明NS-LCR是一个模型无关框架，可无缝嵌入多种法律检索模型。为展示NS-LCR的优越性，我们通过添加人工标注的逻辑规则并引入基于大语言模型（LLM）的新型可解释性评估指标，对现有基准进行了增强。全面的实验表明，NS-LCR在实现高效排序的同时，能够为法律案例检索提供可靠解释。