Co-Matching: Towards human–model collaborative legal case matching

Publication Type

Journal Article

Version

publishedVersion

Publication Date

7-2026

Abstract

Recent efforts have aimed to improve AI models in legal case matching by integrating legal domain knowledge. However, successful legal case matching requires the tacit knowledge of legal practitioners, which is difficult to verbalize and encode into models. This emphasizes the crucial role of involving legal practitioners in high-stakes legal case matching. To address this, we propose a collaborative matching framework called Co-Matching, which encourages both the model and the legal practitioner to participate in the matching process, integrating tacit knowledge. Unlike existing methods that rely solely on the model, Co-Matching allows both the legal practitioner and the model to determine key sentences and then combine them probabilistically. Co-Matching introduces a method called ProtoEM to estimate human decision uncertainty, facilitating the probabilistic combination. Experimental results demonstrate that Co-Matching consistently outperforms existing legal case matching methods, delivering significant performance improvements over human- and model-based matching in isolation (on average, +5.51% and +8.71%, respectively). Further analysis shows that Co-Matching also ensures better human–model collaboration effectiveness. Our study represents an effort in human–model collaboration for the legal case matching task, marking a milestone for future collaborative matching studies.

Keywords

Collaborative Text Matching, Human-model Collaboration, Legal Case Matching

Discipline

Artificial Intelligence and Robotics | Law

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

ACM Transactions on Information Systems

Volume

44

Issue

6

First Page

1

Last Page

33

ISSN

1046-8188

Identifier

10.1145/3818676

Publisher

Association for Computing Machinery (ACM)

Additional URL

https://doi.org/10.1145/3818676

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