  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.
