---
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标题: "MD-ProTector: Positioning Multiple Data-Driven Prototypes for LLM-Generated Text Detection"
原文链接: "https://arxiv.org/abs/2608.10459"
发布日期: "2026-08-11"
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入库时间: "2026-08-12T03:17:51.002Z"
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采集批次: "2026年8月12日2点04分20秒"
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去重键: "https://arxiv.org/abs/2608.10459"
---

## Computer Science > Computation and Language

## Title:MD-ProTector: Positioning Multiple Data-Driven Prototypes for LLM-Generated Text Detection

Authors:[Jinmo Han](https://arxiv.org/search/cs?searchtype=author&query=Han,+J), [Jimin Hong](https://arxiv.org/search/cs?searchtype=author&query=Hong,+J), [Chanyeong Moon](https://arxiv.org/search/cs?searchtype=author&query=Moon,+C), [Ju Yeon Kang](https://arxiv.org/search/cs?searchtype=author&query=Kang,+J+Y), [Seonuk Kim](https://arxiv.org/search/cs?searchtype=author&query=Kim,+S), [Nam Soo Kim](https://arxiv.org/search/cs?searchtype=author&query=Kim,+N+S)

[View PDF](https://arxiv.org/pdf/2608.10459) [HTML (experimental)](https://arxiv.org/html/2608.10459v1)

> 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](https://arxiv.org/abs/2608.10459) \[cs.CL\] |
|  | (or [arXiv:2608.10459v1](https://arxiv.org/abs/2608.10459v1) \[cs.CL\] for this version) |
|  | [https://doi.org/10.48550/arXiv.2608.10459](https://doi.org/10.48550/arXiv.2608.10459) |

## Submission history

From: Jinmo Han \[[view email](https://arxiv.org/show-email/094c3a3a/2608.10459)\]  
**\[v1\]** Tue, 11 Aug 2026 04:14:10 UTC (1,763 KB)

[Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2608.10459) | Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))
