  Legal case retrieval is a critical process for modern legal information
systems. While recent studies have utilized pre-trained language models (PLMs)
based on the general domain self-supervised pre-training paradigm to build
models for legal case retrieval, there are limitations in using general domain
PLMs as backbones. Specifically, these models may not fully capture the
underlying legal features in legal case documents. To address this issue, we
propose CaseEncoder, a legal document encoder that leverages fine-grained legal
knowledge in both the data sampling and pre-training phases. In the data
sampling phase, we enhance the quality of the training data by utilizing
fine-grained law article information to guide the selection of positive and
negative examples. In the pre-training phase, we design legal-specific
pre-training tasks that align with the judging criteria of relevant legal
cases. Based on these tasks, we introduce an innovative loss function called
Biased Circle Loss to enhance the model's ability to recognize case relevance
in fine grains. Experimental results on multiple benchmarks demonstrate that
CaseEncoder significantly outperforms both existing general pre-training models
and legal-specific pre-training models in zero-shot legal case retrieval.
