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MD-ProTector: Positioning Multiple Data-Driven Prototypes for LLM-Generated Text Detection — arXiv:2608.10459

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.10459
License (as archived): CC0-1.0
Archived: 2026-10-19T06:09:11.161Z
SHA-256 of the archived text: db054beb7765972db3101e2e1d03e27995b2b13b4dd2aba94c71421967b944f4
Stable URL of this page: https://metraipolis.com/documento/dc_6ca376fe85cb

How to cite

MD-ProTector: Positioning Multiple Data-Driven Prototypes for LLM-Generated Text Detection — arXiv:2608.10459 — arXiv (en), https://arxiv.org/abs/2608.10459 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_6ca376fe85cb (Metraipolis archive dc_6ca376fe85cb, sha256 db054beb7765972db3101e2e1d03e27995b2b13b4dd2aba94c71421967b944f4)

BibTeX

@misc{dc6ca376fe85cb,
  title = {MD-ProTector: Positioning Multiple Data-Driven Prototypes for LLM-Generated Text Detection — arXiv:2608.10459},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.10459},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_6ca376fe85cb; sha256 db054beb7765972db3101e2e1d03e27995b2b13b4dd2aba94c71421967b944f4},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.10459. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-12 in cs.AI).
Categorie arXiv: Computation and Language (cs.CL) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2608.10459 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T05:57:28.369Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-08-12?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).