TempParaphraser: "Heating Up" Text to Evade AI-Text Detection through Paraphrasing

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 Temp-Paraphraser, a simple yet effective paraphrasing framework that simulates high-temperature sampling effects through multiple normaltemperature 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 https://github.com/HJJWorks/ TempParaphraser .