@inproceedings{an-etal-2025-grex,
    title = "{GR}e{X}: A Graph Neural Network-Based Rerank-then-Expand Method for Detecting Conflicts Among Legal Articles in {K}orean Criminal Law",
    author = "An, Seonho  and
      Rhim, Young-Yik  and
      Kim, Min-Soo",
    editor = "Aletras, Nikolaos  and
      Chalkidis, Ilias  and
      Barrett, Leslie  and
      Goanț{\u{a}}, C{\u{a}}t{\u{a}}lina  and
      Preoțiuc-Pietro, Daniel  and
      Spanakis, Gerasimos",
    booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.nllp-1.30/",
    pages = "408--423",
    ISBN = "979-8-89176-338-8",
    abstract = "As social systems become more complex, legal articles have grown increasingly intricate, making it harder for humans to identify potential conflicts among them, particularly when drafting new laws or applying existing ones. Despite its importance, no method has been proposed to detect such conflicts. We introduce a new legal NLP task, Legal Article Conflict Detection (LACD), which aims to identify conflicting articles within a given body of law. To address this task, we propose GReX, a novel graph neural network-based retrieval method. Experimental results show that GReX significantly outperforms existing methods, achieving improvements of 44.8{\%} in nDCG@50, 32.8{\%} in Recall@50, and 39.8{\%} in Retrieval F1@50. Our codes are in github.com/asmath472/LACD-public."
}