Abstract
Legal Article Prediction (LAP) is a critical task in legal text classification, leveraging natural language processing (NLP) techniques to automatically predict relevant legal articles based on the fact descriptions of cases. As a foundational step in legal decision-making, LAP plays a pivotal role in determining subsequent judgments, such as charges and penalties. Despite its importance, existing methods face significant challenges in addressing the complexities of LAP. Supervised classification models (SCMs), such as CNN and BERT, struggle to fully capture intricate fact patterns due to their inherent limitations. Conversely, large language models (LLMs), while excelling in generative tasks, perform suboptimally in predictive scenarios due to the abstract and ID-based nature of legal articles. Furthermore, the diversity of legal systems across jurisdictions exacerbates the issue, as most approaches are tailored to specific countries and lack broader applicability. To address these limitations, we propose Uni-LAP, a universal framework for legal article prediction that integrates the strengths of SCMs and LLMs through tight collaboration. Specifically, in Uni-LAP, the SCM is enhanced with a novel Top-K loss function to generate accurate candidate articles, while the LLM employs syllogism-inspired reasoning to refine the final predictions. We evaluated Uni-LAP on datasets from multiple jurisdictions, and empirical results demonstrate that our approach consistently outperforms existing baselines, showcasing its effectiveness and generalizability.
Abstract (translated)
法律文章预测（LAP）是法律文本分类中的关键任务，通过运用自然语言处理（NLP）技术来自动预测与案件事实描述相关的法律条文。作为法律决策的基础步骤，LAP在确定后续判决如指控和处罚方面发挥着核心作用。尽管它的重要性不言而喻，现有的方法在解决LAP的复杂性方面面临重大挑战。 监督分类模型（SCMs），例如卷积神经网络（CNN）和BERT，在捕捉复杂的事实模式时往往力有未逮，这主要是由于它们自身的局限性所致。另一方面，大型语言模型（LLMs）虽然在生成任务中表现出色，但在预测场景中的表现却不如人意，这是由法律条文的抽象性和基于ID的特点决定的。此外，不同法域之间法律系统的多样性进一步加剧了这一问题，因为大多数方法都是针对特定国家设计的，并不具备广泛的适用性。 为了解决这些局限性，我们提出了Uni-LAP，这是一个用于法律文章预测的通用框架，通过紧密合作整合了SCMs和LLMs的优势。具体而言，在Uni-LAP中，监督分类模型利用一种新颖的Top-K损失函数来生成准确的候选条文，而大型语言模型则运用类比于三段论的推理方法来优化最终预测结果。 我们在来自多个法域的数据集上评估了Uni-LAP，并通过实验证明我们的方法在各种基准测试中均表现优异，展示了其有效性和普适性。
URL
https://arxiv.org/abs/2509.22119
