{"ID":2871114,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.12385","arxiv_id":"2509.12385","title":"SENTRA: Selected-Next-Token Transformer for LLM Text Detection","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.","short_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-Ne...","url_abs":"https://arxiv.org/abs/2509.12385","url_pdf":"https://arxiv.org/pdf/2509.12385v2","authors":"[\"Mitchell Plyler\",\"Yilun Zhang\",\"Alexander Tuzhilin\",\"Saoud Khalifah\",\"Sen Tian\"]","published":"2025-09-15T19:26:17Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.LG\"]","methods":"[\"Transformer\",\"Large Language Model\"]","has_code":false}