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.

通过使用逻辑规则解释法律案件检索结果，提供合乎逻辑、真实可信且解释性强的法律案件检索模型。