@inproceedings{li-etal-2025-unilr,
title = "{U}ni{LR}: Unleashing the Power of {LLM}s on Multiple Legal Tasks with a Unified Legal Retriever",
author = "Li, Ang and
Wu, Yiquan and
Liu, Yifei and
Cai, Ming and
Qing, Lizhi and
Wang, Shihang and
Kang, Yangyang and
Liu, Chengyuan and
Wu, Fei and
Kuang, Kun",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.584/",
doi = "10.18653/v1/2025.acl-long.584",
pages = "11953--11967",
ISBN = "979-8-89176-251-0",
abstract = "Despite the impressive capabilities of LLMs, they often generate content with factual inaccuracies in LegalAI, which may lead to serious legal consequences. Retrieval-Augmented Generation (RAG), a promising approach, can conveniently integrate specialized knowledge into LLMs. In practice, there are diverse legal knowledge retrieval demands (e.g. law articles and similar cases). However, existing retrieval methods are either designed for general domains, struggling with legal knowledge, or tailored for specific legal tasks, unable to handle diverse legal knowledge types. Therefore, we propose a novel **Uni**fied **L**egal **R**etriever (UniLR) capable of performing multiple legal retrieval tasks for LLMs. Specifically, we introduce attention supervision to guide the retriever in focusing on key elements during knowledge encoding. Next, we design a graph-based method to integrate meta information through a heterogeneous graph, further enriching the knowledge representation. These two components work together to enable UniLR to capture the essence of knowledge hidden beneath formats. Extensive experiments on multiple datasets of common legal tasks demonstrate that UniLR achieves the best retrieval performance and can significantly enhance the performance of LLM."
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<abstract>Despite the impressive capabilities of LLMs, they often generate content with factual inaccuracies in LegalAI, which may lead to serious legal consequences. Retrieval-Augmented Generation (RAG), a promising approach, can conveniently integrate specialized knowledge into LLMs. In practice, there are diverse legal knowledge retrieval demands (e.g. law articles and similar cases). However, existing retrieval methods are either designed for general domains, struggling with legal knowledge, or tailored for specific legal tasks, unable to handle diverse legal knowledge types. Therefore, we propose a novel **Uni**fied **L**egal **R**etriever (UniLR) capable of performing multiple legal retrieval tasks for LLMs. Specifically, we introduce attention supervision to guide the retriever in focusing on key elements during knowledge encoding. Next, we design a graph-based method to integrate meta information through a heterogeneous graph, further enriching the knowledge representation. These two components work together to enable UniLR to capture the essence of knowledge hidden beneath formats. Extensive experiments on multiple datasets of common legal tasks demonstrate that UniLR achieves the best retrieval performance and can significantly enhance the performance of LLM.</abstract>
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%0 Conference Proceedings
%T UniLR: Unleashing the Power of LLMs on Multiple Legal Tasks with a Unified Legal Retriever
%A Li, Ang
%A Wu, Yiquan
%A Liu, Yifei
%A Cai, Ming
%A Qing, Lizhi
%A Wang, Shihang
%A Kang, Yangyang
%A Liu, Chengyuan
%A Wu, Fei
%A Kuang, Kun
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F li-etal-2025-unilr
%X Despite the impressive capabilities of LLMs, they often generate content with factual inaccuracies in LegalAI, which may lead to serious legal consequences. Retrieval-Augmented Generation (RAG), a promising approach, can conveniently integrate specialized knowledge into LLMs. In practice, there are diverse legal knowledge retrieval demands (e.g. law articles and similar cases). However, existing retrieval methods are either designed for general domains, struggling with legal knowledge, or tailored for specific legal tasks, unable to handle diverse legal knowledge types. Therefore, we propose a novel **Uni**fied **L**egal **R**etriever (UniLR) capable of performing multiple legal retrieval tasks for LLMs. Specifically, we introduce attention supervision to guide the retriever in focusing on key elements during knowledge encoding. Next, we design a graph-based method to integrate meta information through a heterogeneous graph, further enriching the knowledge representation. These two components work together to enable UniLR to capture the essence of knowledge hidden beneath formats. Extensive experiments on multiple datasets of common legal tasks demonstrate that UniLR achieves the best retrieval performance and can significantly enhance the performance of LLM.
%R 10.18653/v1/2025.acl-long.584
%U https://aclanthology.org/2025.acl-long.584/
%U https://doi.org/10.18653/v1/2025.acl-long.584
%P 11953-11967
Markdown (Informal)
[UniLR: Unleashing the Power of LLMs on Multiple Legal Tasks with a Unified Legal Retriever](https://aclanthology.org/2025.acl-long.584/) (Li et al., ACL 2025)
ACL
- Ang Li, Yiquan Wu, Yifei Liu, Ming Cai, Lizhi Qing, Shihang Wang, Yangyang Kang, Chengyuan Liu, Fei Wu, and Kun Kuang. 2025. UniLR: Unleashing the Power of LLMs on Multiple Legal Tasks with a Unified Legal Retriever. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 11953–11967, Vienna, Austria. Association for Computational Linguistics.