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To combat the potential misuse of Natural Language Generation (NLG)technology, a variety of algorithms have been developed for the detection ofAI-generated texts. Traditionally, this task is treated as a binaryclassification problem. Although supervised learning has demonstrated promisingresults, acquiring labeled data for detection purposes poses real-worldchallenges and the risk of overfitting. In an effort to address these issues,we delve into the realm of zero-shot machine-generated text detection. Existingzero-shot detectors, typically designed for specific tasks or topics, oftenassume uniform testing scenarios, limiting their practicality. In our research,we explore various advanced Large Language Models (LLMs) and their specializedvariants, contributing to this field in several ways. In empirical studies, weuncover a significant correlation between topics and detection performance.Secondly, we delve into the influence of topic shifts on zero-shot detectors.These investigations shed light on the adaptability and robustness of thesedetection methods across diverse topics. The code is available at\url{https://github.com/yfzhang114/robustness-detection}.

Assaying on the Robustness of Zero-Shot Machine-Generated Text Detectors