@inproceedings{plyler-etal-2025-sentra,
    title = "{SENTRA}: Selected-Next-Token Transformer for {LLM} Text Detection",
    author = "Plyler, Mitchell  and
      Zhang, Yilun  and
      Tuzhilin, Alexander  and
      Khalifah, Saoud  and
      Tian, Sen",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.findings-emnlp.1004/",
    doi = "10.18653/v1/2025.findings-emnlp.1004",
    pages = "18499--18516",
    ISBN = "979-8-89176-335-7",
    abstract = "LLMs are becoming increasingly capable and widespread. Consequently, the potential and reality of their misuse is also growing. In this work, we address the problem of detecting LLM-generated text that is not explicitly declared as such. We present a novel, general-purpose, and supervised LLM text detector, \textit{SElected-Next-Token tRAnsformer (SENTRA)}. SENTRA is a Transformer-based encoder leveraging selected-next-token-probability sequences and utilizing contrastive pre-training on large amounts of unlabeled data. Our experiments on three popular public datasets across 24 domains of text demonstrate SENTRA is a general-purpose classifier that significantly outperforms popular baselines in the out-of-domain setting."
}