逻辑规则作为法律案例检索的解释
Logic Rules as Explanations for Legal Case Retrieval
AI总结：
针对法律案例检索缺乏忠实、逻辑化解释的问题，提出模型无关的神经符号框架 NS-LCR，通过案件级与法律级逻辑规则进行匹配推理，并在增强基准上验证其排序和解释能力。
摘要：
本文解决使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）高度专业化，要求系统在作出法律决定前提供合乎逻辑、忠实且可解释的说明。近来，已有研究致力于学习可解释的法律案例检索模型。然而，这些方法通常从法律案件中选取理据（关键句）作为解释，无法提供忠实且逻辑正确的解释。本文提出 Neural-Symbolic enhanced Legal Case Retrieval（NS-LCR，神经符号增强法律案例检索）框架，通过学习案件级和法律级逻辑规则，对法律案件匹配进行显式推理。所学规则随后以神经符号方式整合到检索过程中。得益于逻辑规则的逻辑性和可解释性，NS-LCR 具备内置的忠实可解释性。我们还表明，NS-LCR 是一个模型无关框架，可插入多个法律检索模型。为展示 NS-LCR 的优越性，我们通过添加人工标注的逻辑规则增强现有基准，并引入一种使用 Large Language Models（LLM，大语言模型）的新型可解释性指标。综合实验揭示了 NS-LCR 在排序方面的有效性，以及它为法律案例检索提供可靠解释的能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
