UQLegalAI@COLIEE2025: Advancing Legal Case Retrieval with Large Language Models and Graph Neural Networks

TL;DR

This paper introduces CaseLink, a novel legal case retrieval method combining large language models and graph neural networks, achieving high accuracy in COLIEE 2025 by leveraging case connectivity and a new contrastive learning objective.

Contribution

The paper presents a new approach that integrates large language models with inductive graph learning and a contrastive objective for improved legal case retrieval accuracy.

Findings

Achieved second place in COLIEE 2025 Task 1

Utilized large language models for effective text embeddings

Proposed a contrastive learning objective with degree regularization

Abstract

Legal case retrieval plays a pivotal role in the legal domain by facilitating the efficient identification of relevant cases, supporting legal professionals and researchers to propose legal arguments and make informed decision-making. To improve retrieval accuracy, the Competition on Legal Information Extraction and Entailment (COLIEE) is held annually, offering updated benchmark datasets for evaluation. This paper presents a detailed description of CaseLink, the method employed by UQLegalAI, the second highest team in Task 1 of COLIEE 2025. The CaseLink model utilises inductive graph learning and Global Case Graphs to capture the intrinsic case connectivity to improve the accuracy of legal case retrieval. Specifically, a large language model specialized in text embedding is employed to transform legal texts into embeddings, which serve as the feature representations of the nodes in the constructed case graph. A new contrastive objective, incorporating a regularization on the degree of case nodes, is proposed to leverage the information within the case reference relationship for model optimization. The main codebase used in our method is based on an open-sourced repo of CaseLink: https://github.com/yanran-tang/CaseLink.