SENTRA: Selected-Next-Token Transformer for LLM Text Detection
Mitchell Plyler, Yilun Zhang, Alexander Tuzhilin, Saoud Khalifah, Sen Tian
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, 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.
BibTeX
@inproceedings{emnlp2025_sentraselectedne,
title = {SENTRA: Selected-Next-Token Transformer for LLM Text Detection},
author = {Mitchell Plyler and Yilun Zhang and Alexander Tuzhilin and Saoud Khalifah and Sen Tian},
booktitle = {EMNLP 2025},
year = {2025}
}