@inproceedings{yang-etal-2026-beyond-case,
title = "Beyond Case-Level Similarity: Multi-Defendant Legal Case Retrieval via Individual-Aware Reasoning",
author = "Yang, Yi and
Ma, Ruize and
Feng, Yi and
Li, Chuanyi and
Ng, Vincent",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1918/",
pages = "43688--43713",
ISBN = "979-8-89176-459-0",
abstract = "Legal Case Retrieval (LCR) aims to identify cases that are legally similar to a given query case. Existing research has largely recast LCR as the task of modeling the textual similarity between the query case and a historical case. While this approach can handle single-defendant cases fairly easily, such surface-level matching proves insufficient for multi-defendant cases, which typically requires modeling and aligning defendants' roles and judgment outcomes in different cases. To address the challenges associated with Multi-defendant LCR (Multi-LCR), we (1) present Multi-ELAM and Multi-LeCaRDv2, two multi-defendant datasets we create by enriching widely used benchmarks with defendant-level annotations, and (2) introduce a novel role- and judgment-aware framework for Multi-LCR. Experimental results show that our approach outperforms state-of-the-art baselines, highlighting its effectiveness for Multi-LCR."
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<abstract>Legal Case Retrieval (LCR) aims to identify cases that are legally similar to a given query case. Existing research has largely recast LCR as the task of modeling the textual similarity between the query case and a historical case. While this approach can handle single-defendant cases fairly easily, such surface-level matching proves insufficient for multi-defendant cases, which typically requires modeling and aligning defendants’ roles and judgment outcomes in different cases. To address the challenges associated with Multi-defendant LCR (Multi-LCR), we (1) present Multi-ELAM and Multi-LeCaRDv2, two multi-defendant datasets we create by enriching widely used benchmarks with defendant-level annotations, and (2) introduce a novel role- and judgment-aware framework for Multi-LCR. Experimental results show that our approach outperforms state-of-the-art baselines, highlighting its effectiveness for Multi-LCR.</abstract>
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%0 Conference Proceedings
%T Beyond Case-Level Similarity: Multi-Defendant Legal Case Retrieval via Individual-Aware Reasoning
%A Yang, Yi
%A Ma, Ruize
%A Feng, Yi
%A Li, Chuanyi
%A Ng, Vincent
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F yang-etal-2026-beyond-case
%X Legal Case Retrieval (LCR) aims to identify cases that are legally similar to a given query case. Existing research has largely recast LCR as the task of modeling the textual similarity between the query case and a historical case. While this approach can handle single-defendant cases fairly easily, such surface-level matching proves insufficient for multi-defendant cases, which typically requires modeling and aligning defendants’ roles and judgment outcomes in different cases. To address the challenges associated with Multi-defendant LCR (Multi-LCR), we (1) present Multi-ELAM and Multi-LeCaRDv2, two multi-defendant datasets we create by enriching widely used benchmarks with defendant-level annotations, and (2) introduce a novel role- and judgment-aware framework for Multi-LCR. Experimental results show that our approach outperforms state-of-the-art baselines, highlighting its effectiveness for Multi-LCR.
%U https://aclanthology.org/2026.emnlp-main.1918/
%P 43688-43713
Markdown (Informal)
[Beyond Case-Level Similarity: Multi-Defendant Legal Case Retrieval via Individual-Aware Reasoning](https://aclanthology.org/2026.emnlp-main.1918/) (Yang et al., EMNLP 2026)
ACL