A Reproducibility Study of Graph-Based Legal Case Retrieval

• URL: http://arxiv.org/abs/2504.08400v1
• Date: Fri, 11 Apr 2025 10:04:12 GMT
• Title: A Reproducibility Study of Graph-Based Legal Case Retrieval
• Authors: Gregor Donabauer, Udo Kruschwitz,
• Abstract summary: CaseLink is a graph-based method for legal case retrieval.<n>CaseLink captures higher-order relationships of cases going beyond the stand-alone level of documents.<n>Challenges in reproducing novel results have recently been highlighted.
• Score: 1.6819960041696331
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Legal retrieval is a widely studied area in Information Retrieval (IR) and a key task in this domain is retrieving relevant cases based on a given query case, often done by applying language models as encoders to model case similarity. Recently, Tang et al. proposed CaseLink, a novel graph-based method for legal case retrieval, which models both cases and legal charges as nodes in a network, with edges representing relationships such as references and shared semantics. This approach offers a new perspective on the task by capturing higher-order relationships of cases going beyond the stand-alone level of documents. However, while this shift in approaching legal case retrieval is a promising direction in an understudied area of graph-based legal IR, challenges in reproducing novel results have recently been highlighted, with multiple studies reporting difficulties in reproducing previous findings. Thus, in this work we reproduce CaseLink, a graph-based legal case retrieval method, to support future research in this area of IR. In particular, we aim to assess its reliability and generalizability by (i) first reproducing the original study setup and (ii) applying the approach to an additional dataset. We then build upon the original implementations by (iii) evaluating the approach's performance when using a more sophisticated graph data representation and (iv) using an open large language model (LLM) in the pipeline to address limitations that are known to result from using closed models accessed via an API. Our findings aim to improve the understanding of graph-based approaches in legal IR and contribute to improving reproducibility in the field. To achieve this, we share all our implementations and experimental artifacts with the community.

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