# CaseLink: Inductive Graph Learning for Legal Case Retrieval

Source: [CaseLink: Inductive Graph Learning for Legal Case Retrieval](<https://www.sota2.com/research/paper/68fa739a1e209131cea5d868>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:57:07.240Z

Authors: Yanran Tang, Ruihong Qiu, Hongzhi Yin, Xue Li, Zi Huang

Published: 2024-03-26T15:13:16.000Z

## Abstract

In case law, the precedents are the relevant cases that are used to support the decisions made by the judges and the opinions of lawyers towards a given case. This relevance is referred to as the case-to-case reference relation. To efficiently find relevant cases from a large case pool, retrieval tools are widely used by legal practitioners. Existing legal case retrieval models mainly work by comparing the text representations of individual cases. Although they obtain a decent retrieval accuracy, the intrinsic case connectivity relationships among cases have not been well exploited for case encoding, therefore limiting the further improvement of retrieval performance. In a case pool, there are three types of case connectivity relationships: the case reference relationship, the case semantic relationship, and the case legal charge relationship. Due to the inductive manner in the task of legal case retrieval, using case reference as input is not applicable for testing. Thus, in this paper, a CaseLink model based on inductive graph learning is proposed to utilise the intrinsic case connectivity for legal case retrieval, a novel Global Case Graph is incorporated to represent both the case semantic relationship and the case legal charge relationship. A novel contrastive objective with a regularisation on the degree of case nodes is proposed to leverage the information carried by the case reference relationship to optimise the model. Extensive experiments have been conducted on two benchmark datasets, which demonstrate the state-of-the-art performance of CaseLink. The code has been released on https://github.com/yanran-tang/CaseLink.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Legal Case Retrieval on COLIEE Top-5 2022](<https://www.sota2.com/research/sota/legal-case-retrieval-on-coliee-top-5-2022.md>): Legal Case Retrieval; COLIEE Top-5 2022; P@5: 37.

- [Legal Case Retrieval on COLIEE 2023](<https://www.sota2.com/research/sota/legal-case-retrieval-on-coliee-2023.md>): Legal Case Retrieval; COLIEE 2023; P@5: 21.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2403.17780>)

[PDF](<https://arxiv.org/pdf/2403.17780.pdf>)