@inproceedings{hu-etal-2026-judge,
    title = "To Judge or Not to Judge: Can Large Language Models Leverage the Dispute Focus in Legal Judgment?",
    author = "Hu, Luoming  and
      Yang, Liang  and
      Zeng, Jingjie  and
      Xing, Zijie",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1410/",
    pages = "30550--30571",
    ISBN = "979-8-89176-390-6",
    abstract = "Civil judicial cases are highly complicated, posing significant challenges for Large Language Models (LLMs) for Legal Judgment Prediction (LJP). While judges manage this complexity through the dispute focus{---}a mechanism distilling cases into core issues{---}existing research largely overlooks this tool in favor of generic reasoning frameworks that lack authentic judicial logic. To bridge this gap, we first introduce $\textbf{FocalLaw}$, the first dataset aligning full-process Chinese civil judicial data through the dispute focus, comprising 1,000 high-quality cases across six causes of action. Building on this dataset, we examine LLMs' capability to utilize the dispute focus and uncover a counter-intuitive phenomenon: LLMs fail to leverage the dispute focus even with CoT and SFT, which we identify as the ``Clerk Trap''.To solve the problem, we propose $\textbf{FocalJudge}$, a novel framework that leverages the dispute focus to guide LLMs through a structured, judge-like cognitive workflow. Experimental results demonstrate the effectiveness of FocalJudge and offer valuable insights into the interpretability and reliability of LLMs in the legal domain."
}