@inproceedings{t-y-s-s-elganayni-2025-promalex,
    title = "{P}ro{MAL}ex: Progressive Modular Adapters for Multi-Jurisdictional Legal Language Modeling",
    author = "T.y.s.s, Santosh  and
      Elganayni, Mohamed Hesham",
    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.1080/",
    pages = "22201--22217",
    ISBN = "979-8-89176-251-0",
    abstract = "This paper addresses the challenge of adapting language models to the jurisdiction-specific nature of legal corpora. Existing approaches{---}training separate models for each jurisdiction or using a single shared model{---}either fail to leverage common legal principles beneficial for low-resource settings or risk negative interference from conflicting jurisdictional interpretations. To overcome these limitations, we propose a parameter-efficient framework ProMALex, that first derives hierarchical relationships across jurisdictions and progressively inserts adapter modules across model layers based on jurisdictional similarity. This design allows modules in lower layers to be shared across jurisdictions, capturing common legal principles, while higher layers specialize through jurisdiction-specific adapters. Experimental results on two legal language modeling benchmarks demonstrate that ProMALex outperforms both fully shared and jurisdiction-specific models."
}