@inproceedings{xu-etal-2025-clear,
    title = "{CLEAR}: A Framework Enabling Large Language Models to Discern Confusing Legal Paragraphs",
    author = "Xu, Qi  and
      Liu, Qian  and
      Fei, Hao  and
      Yu, Hang  and
      Guan, Shuhao  and
      Wei, Xiao",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.475/",
    doi = "10.18653/v1/2025.findings-emnlp.475",
    pages = "8937--8953",
    ISBN = "979-8-89176-335-7",
    abstract = "Most of the existing work focuses on enabling LLMs to leverage legal rules (, law articles) to tackle complex legal reasoning tasks, but ignores their ability to understand legal rules. To better evaluate the LLMs' capabilities on the task, in this work, we propose a new challenge task: Legal Paragraph Prediction (LPP), which aims to predict the legal paragraph given criminal facts. Moreover, to enhance the legal reasoning ability of LLMs, we propose a novel framework CLEAR, enabling LLMs to analyze legal cases with the guidance of legal rule insights. The CLEAR contains four key components, where the \textit{Legal Rules Retriever} aims to retrieve legal rule knowledge, and the \textit{Rule Insights Generator} is used to generate legal insights guiding the LLM{'}s reasoning, then the \textit{Case Analyzer} analyze the case with the guidance of legal rule insights given criminal facts. Finally, the \textit{Legal Reasoner} synthesizes the criminal facts, legal rule insights, and analysis results to derive the final decision. By conducting extensive experiments on a real-world dataset, experimental results validate the effectiveness of our proposed model. Our codes and dataset are available at \url{https://anonymous.4open.science/r/CLEAR-3048}."
}