MD-ProTector：基于多数据驱动原型定位的大语言模型生成文本检测方法

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Abstract: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.

Subjects:	Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as:	arXiv:2608.10459 [cs.CL]
	(or arXiv:2608.10459v2 [cs.CL] for this version)
	https://doi.org/10.48550/arXiv.2608.10459 arXiv-issued DOI via DataCite

Submission history

From: Jinmo Han [view email]
[v1] Tue, 11 Aug 2026 04:14:10 UTC (1,763 KB)
[v2] Tue, 3 Nov 2026 20:02:49 UTC (1,766 KB)