Abstract
Legal text classification is a fundamental NLP task in the legal domain. Benchmark datasets in this area often exhibit a long-tail label distribution, where many labels are underrepresented, leading to poor model performance on rare classes. This paper proposes Selective Retrieval-Augmentation (SRA) as a solution to this problem. SRA focuses on augmenting samples belonging to low-frequency labels in the training set, preventing the introduction of noise for well-represented classes, and requires no changes to the model architecture. Retrieval is performed only from the training data to ensure there is no potential information leakage, removing the need for external corpora simultaneously. The proposed SRA method is tested on two legal text classification benchmark datasets with long-tail distributions: LEDGAR (single-label) and UNFAIR-ToS (multi-label). The results indicate that SRA attains higher micro-F1 and macro-F1 scores compared to all current LexGLUE baselines across both datasets, illustrating consistent improvements in long-tail legal text classification. The code repository is available at: this https URL
Abstract (translated)
法律文本分类是法律领域中的基本自然语言处理任务。在这个领域的基准数据集中，标签分布往往呈现出长尾特性，即许多标签的代表性不足，导致模型在罕见类别上的表现较差。本文提出了一种名为选择性检索增强（Selective Retrieval-Augmentation，简称SRA）的方法来解决这个问题。SRA专注于增强训练集中的低频标签样本，同时避免为那些已有足够代表性的类引入噪声，并且无需对模型架构进行任何更改。检索仅从训练数据中进行，以确保没有潜在的信息泄露风险，并且不需要外部语料库。 本文提出的SRA方法在两个具有长尾分布的法律文本分类基准数据集上进行了测试：LEDGAR（单标签）和UNFAIR-ToS（多标签）。实验结果显示，在这两个数据集中，与所有当前LexGLUE基线相比，SRA获得了更高的微平均F1分数和宏平均F1分数，表明其在长尾法律文本分类方面取得了持续的改进。该方法的代码库可在此处获得：[此链接](https://this https URL)。 请注意，原文中的“this https URL”应更正为实际的有效链接地址以提供访问相关代码资源的可能性。
URL
https://arxiv.org/abs/2508.19997
