| Challenge: | Existing methods for detecting AIGTs focus on probabilistic features, causing problems . performance of black-box detectors is low, and it is difficult to detect black- box models . |
| Approach: | They propose a detector that can discriminate multiple black-box LLMs without probabilistic features and the origin of AIGT. |
| Outcome: | The proposed method can discriminate multiple black-box LLMs without probabilistic features and the origin of AIGT. |
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| Challenge: | Recent research indicates that AI-text detection systems lack robustness and struggle to effectively differentiate perturbed texts. |
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A Practical Examination of AI-Generated Text Detectors for Large Language Models (2025.findings-naacl)
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Yuxia Wang, Jonibek Mansurov, Petar Ivanov, Jinyan Su, Artem Shelmanov, Akim Tsvigun, Chenxi Whitehouse, Osama Mohammed Afzal, Tarek Mahmoud, Toru Sasaki, Thomas Arnold, Alham Aji, Nizar Habash, Iryna Gurevych, Preslav Nakov
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Are We in the AI-Generated Text World Already? Quantifying and Monitoring AIGT on Social Media (2025.acl-long)
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Hanxi Guo, Siyuan Cheng, Xiaolong Jin, Zhuo Zhang, Guangyu Shen, Kaiyuan Zhang, Shengwei An, Guanhong Tao, Xiangyu Zhang
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Kill two birds with one stone: generalized and robust AI-generated text detection via dynamic perturbations (2025.naacl-long)
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| Challenge: | Existing methods focus on model generalization or focus on robustness. |
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EvoBench: Towards Real-world LLM-Generated Text Detection Benchmarking for Evolving Large Language Models (2025.findings-acl)
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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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SeqXGPT: Sentence-Level AI-Generated Text Detection (2023.emnlp-main)
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| Challenge: | Existing methods for sentence-level AIGT detection are weak . large language models (LLMs) can generate human-like content . |
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