@inproceedings{huang-etal-2025-tempparaphraser,
    title = "{T}emp{P}araphraser: ``Heating Up'' Text to Evade {AI}-Text Detection through Paraphrasing",
    author = "Huang, Junjie  and
      Zhang, Ruiquan  and
      Su, Jinsong  and
      Chen, Yidong",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1607/",
    pages = "31542--31561",
    ISBN = "979-8-89176-332-6",
    abstract = "The widespread adoption of large language models (LLMs) has increased the need for reliable AI-text detection. While current detectors perform well on benchmark datasets, we highlight a critical vulnerability: increasing the temperature parameter during inference significantly reduces detection accuracy. Based on this weakness, we propose TempParaphraser, a simple yet effective paraphrasing framework that simulates high-temperature sampling effects through multiple normal-temperature generations, effectively evading detection. Experiments show that TempParaphraser reduces detector accuracy by an average of 82.5{\%} while preserving high text quality. We also demonstrate that training on TempParaphraser-augmented data improves detector robustness. All resources are publicly available at \url{https://github.com/HJJWorks/TempParaphraser}."
}