SENTRA: Selected-Next-Token Transformer for LLM Text Detection (2025.findings-emnlp)
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| Challenge: | SENTRA is a general-purpose, supervised LLM text detector . it detects LLM-generated text that is not explicitly declared as such . |
| Approach: | They propose a general-purpose LLM text detector that detects unlabeled text . they use a transformer-based encoder that leverages selected-next-token sequences . |
| Outcome: | The proposed classifier outperforms baselines on 24 domains of text. |
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| Challenge: | Existing methods to detect LLM-generated texts rely on static benchmarks that neglect the evolving nature of LLMs. |
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| Challenge: | Existing detection tools rely on access to LLMs and can only distinguish between machine-generated and human-authored text. |
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| Challenge: | Out-of-distribution (OOD) detection is a crucial part of deep neural networks. |
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LLM-DetectAIve: a Tool for Fine-Grained Machine-Generated Text Detection (2024.emnlp-demo)
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Mervat Abassy, Kareem Elozeiri, Alexander Aziz, Minh Ta, Raj Tomar, Bimarsha Adhikari, Saad Ahmed, Yuxia Wang, Osama Mohammed Afzal, Zhuohan Xie, Jonibek Mansurov, Ekaterina Artemova, Vladislav Mikhailov, Rui Xing, Jiahui Geng, Hasan Iqbal, Zain Mujahid, Tarek Mahmoud, Akim Tsvigun, Alham Aji, Artem Shelmanov, Nizar Habash, Iryna Gurevych, Preslav Nakov
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