@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://preview.aclanthology.org/ingestion-acl-25/2025.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."
}