CLEAR : A Framework Enabling Large Language Models to Discern Confusing Legal Paragraphs (2025)

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 Legal Rules Retriever aims to retrieve legal rule knowledge, and the Rule Insights Generator is used to generate legal insights guiding the LLM’s reasoning, then the Case Analyzer analyze the case with the guidance of legal rule insights given criminal facts. Finally, the 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 https://anonymous.4open.science/r/CLEAR-3048 .

## introduction large language models (llms) have achieved remarkable performance across various natural language processing (nlp) tasks @xcite @xcite @xcite . however, prior studies show that they often struggle to capture the complexities of real-world applications, particularly in specialized domains such as law @xcite @xcite . ## lpp dataset data collection. due to the absence of the paragraph prediction dataset, to fill this gap, we construct our dataset lpp. cases from lpp are sourced from the government website china judgment online (cjo) 1 , which is widely used in the legalai tasks @xcite . follow- ## conclusion in this study, we introduce a novel framework clear, containing four components: the legal rules retriever module aims to retrieve candidate legal paragraphs. the rule insight generator aims to prompt llms to raise questions to distinguish them. the case analyzer aims to generate case insights by answering the questions. finally, the legal reasoner concludes by integrating legal knowledge and legal insights. comprehensive experiment results demonstrate the effectiveness of our clear.