  To combat the potential misuse of Natural Language Generation (NLG)
technology, a variety of algorithms have been developed for the detection of
AI-generated texts. Traditionally, this task is treated as a binary
classification problem. Although supervised learning has demonstrated promising
results, acquiring labeled data for detection purposes poses real-world
challenges and the risk of overfitting. In an effort to address these issues,
we delve into the realm of zero-shot machine-generated text detection. Existing
zero-shot detectors, typically designed for specific tasks or topics, often
assume uniform testing scenarios, limiting their practicality. In our research,
we explore various advanced Large Language Models (LLMs) and their specialized
variants, contributing to this field in several ways. In empirical studies, we
uncover 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 these
detection methods across diverse topics. The code is available at
\url{https://github.com/yfzhang114/robustness-detection}.
