As an important component of intelligent legal systems, legal case retrieval plays a critical role in ensuring judicial justice and fairness. However, the development of legal case retrieval technologies in the Chinese legal system is restricted by three problems in existing datasets: limited data size, narrow definitions of legal relevance, and naive candidate pooling strategies used in data sampling. To alleviate these issues, we introduce LeCaRDv2, a large-scale Legal Case Retrieval Dataset (version 2). It consists of 800 queries and 55,192 candidates extracted from 4.3 million criminal case documents. To the best of our knowledge, LeCaRDv2 is one of the largest Chinese legal case retrieval datasets, providing extensive coverage of criminal charges. Additionally, we enrich the existing relevance criteria by considering three key aspects: characterization, penalty, procedure. This comprehensive criteria enriches the dataset and may provides a more holistic perspective. Furthermore, we propose a two-level candidate set pooling strategy that effectively identify potential candidates for each query case. It's important to note that all cases in the dataset have been annotated by multiple legal experts specializing in criminal law. Their expertise ensures the accuracy and reliability of the annotations. We evaluate several state-of-the-art retrieval models at LeCaRDv2, demonstrating that there is still significant room for improvement in legal case retrieval. The details of LeCaRDv2 can be found at the anonymous website https://github.com/anonymous1113243/LeCaRDv2.

翻译：作为智能法律系统的重要组成部分，法律案例检索在保障司法公正与公平中发挥着关键作用。然而，现有数据集存在的三个问题制约了中文法律体系下法律案例检索技术的发展：数据规模有限、法律相关性定义过于狭窄、以及数据采样中采用的朴素候选池策略。为缓解这些问题，我们提出了LeCaRDv2——一个大规模法律案例检索数据集（第二版）。该数据集包含从430万份刑事案例文书中提取的800个查询案例与55192个候选案例。据我们所知，LeCaRDv2是规模最大的中文法律案例检索数据集之一，提供了对刑事罪名的广泛覆盖。此外，我们从三个关键维度——定性、量刑、程序——丰富了现有相关性标准，该综合性标准增强了数据集内容，并可能提供更全面的视角。进一步地，我们提出了一种两级候选集池化策略，能够有效识别每个查询案例的潜在候选案例。值得强调的是，数据集中所有案例均由多名专攻刑法的法律专家完成标注，其专业知识确保了标注的准确性与可靠性。我们在LeCaRDv2上评估了多种先进检索模型，结果表明法律案例检索仍存在显著改进空间。LeCaRDv2的详细信息可访问匿名网站https://github.com/anonymous1113243/LeCaRDv2获取。