Zero-Shot Detection of LLM-Generated Text using Temperature Sensitivity

Abstract

The widespread deployment of Large Language Models (LLMs) has spurred significant progress in the detection of LLM-generated text. However, existing detection methods often rely on statistical features that are insufficient for reliable detection; for example, even though LLM-generated and human-written texts exhibit different probability distributions in surrogate models, they can produce nearly identical entropy values, thereby conflating the two types of text. In this paper, we propose that modulating the decoding temperature and monitoring how the probability distributions respond can better probe the intrinsic discrepancies between two types of text. Building upon this insight, we introduce a new feature termed Temperature Sensitivity (TS) and demonstrate that LLM-generated text tends to exhibit higher TS than human-written text. Finally, we propose NTS, a novel and simple zero-shot detector built upon normalized temperature sensitivity. Extensive experiments across three datasets, multiple domains, and various source models demonstrate the superior effectiveness and robustness of our proposed approach. Code avaliable at: https:/