ExaGPT: Example-Based Machine-Generated Text Detection for Human Interpretability

Challenge:	Existing interpretable detectors are not aligned with the human decision-making process and fail to offer evidence that users easily understand.
Approach:	They propose an interpretable detection approach that checks whether a text is human-written or LLM-generated by checking whether it shares more similar spans with human-generated texts.
Outcome:	ExaGPT outperforms interpretable detectors by +37.0 points at a false positive rate of 1%.

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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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MAGE: Machine-generated Text Detection in the Wild (2024.acl-long)

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Yafu Li, Qintong Li, Leyang Cui, Wei Bi, Zhilin Wang, Longyue Wang, Linyi Yang, Shuming Shi, Yue Zhang

Language Models

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Machine-generated text detection prevents language model collapse (2025.emnlp-main)

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George Drayson, Emine Yilmaz, Vasileios Lampos

Language Models

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HLU: Human Vs LLM Generated Text Detection Dataset for Urdu at Multiple Granularities (2025.coling-main)

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Iqra Ali, Jesse Atuhurra, Hidetaka Kamigaito, Taro Watanabe

Text Generation

Challenge:	Using large language models (LLMs) to generate human-like text has raised concerns about misuse, especially in low-resource languages like Urdu.
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A Survey on Detection of LLMs-Generated Content (2024.findings-emnlp)

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Xianjun Yang, Liangming Pan, Xuandong Zhao, Haifeng Chen, Linda Petzold, William Yang Wang, Wei Cheng

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Nafis Irtiza Tripto, Saranya Venkatraman, Mahjabin Nahar, Dongwon Lee

Text Generation

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Efficient Detection of LLM-generated Texts with a Bayesian Surrogate Model (2024.findings-acl)

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Yibo Miao, Hongcheng Gao, Hao Zhang, Zhijie Deng

Language Models

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Detecting Machine-Generated Long-Form Content with Latent-Space Variables (2024.findings-emnlp)

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Yufei Tian, Zeyu Pan, Nanyun Peng

Language Models

Challenge:	Existing zero-shot methods to distinguish machine-generated long-form texts from humans are vulnerable to domain shift including different decoding strategies, variations in prompts, and attacks.
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People who frequently use ChatGPT for writing tasks are accurate and robust detectors of AI-generated text (2025.acl-long)

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Jenna Russell, Marzena Karpinska, Mohit Iyyer

Responsible & Trustworthy NLP

Challenge:	Qualitative analysis of experts’ free-form explanations shows that while they rely heavily on specific lexical clues (‘AI vocabulary’), they also pick up on more complex phenomena within the text (e.g., formality, originality, clarity).
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