用多个可训练原型提升大模型文本检测的精度与泛化能力。
MD-ProTector: Positioning Multiple Data-Driven Prototypes for LLM-Generated Text Detection
- 为每类文本设计多个可训练原型，细化分类边界。
- 在5个场景下，平均召回率最高，误报率最低。
- 适合需要高精度检测多风格生成文本的场景。
随着大语言模型生成内容愈发逼真，检测系统需在大规模部署中应对多样写作风格、领域、语言及生成模型。仅输入编码器的检测方法适合实际应用，但传统二分类仅提供类别标签，无法显式建模同一类别内的丰富差异。本文提出MD-ProTector，通过在编码器嵌入空间中为每类文本引入多个可训练参考向量（即原型），实现对同类文本内部变异的分组建模。为解决原型应代表何种差异的问题，提出原型定位损失（Prototype Positioning loss），将类别整体结构与类内变化解耦。在涵盖领域、生成器、语言和对抗性变化的三个大规模基准上评估，MD-ProTector在MAGE CDCM和RAID上取得最高平均召回率，在RAID上达到最高AUROC与最低FPR95，优于其他基于编码器的方法。
原文摘要 · Abstract (English)
As LLM-generated content becomes more sophisticated, detection systems for distinguishing those texts from human-written text must operate at scale while handling diverse writing styles, domains, languages, and generator models. Input-only encoder detectors are suitable for practical deployment setting, but standard binary classification supplies only the class label and does not explicitly organize the substantial variation within either class. We propose MD-ProTector, which represents each class with multiple trainable reference vectors in the encoder embedding space, referred to as prototypes. These prototypes provide separate decision boundaries for different groups of texts within the same class. However, adding multiple prototypes alone does not determine which variation each prototype should represent. MD-ProTector addresses this problem with Prototype Positioning loss, which separates class-level structure from the within-class variation that differentiates individual prototypes. Evaluated across five settings from three large-scale benchmarks covering domain, generator, language, and adversarial variation, MD-ProTector achieves the highest AvgRec on MAGE CDCM and RAID and the highest AUROC and lowest FPR95 on RAID among the compared encoder-based methods.
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