Large language models (LLMs) such as ChatGPT are increasingly being used for various use cases, including text content generation at scale. Although detection methods for such AI-generated text exist already, we investigate ChatGPT's performance as a detector on such AI-generated text, inspired by works that use ChatGPT as a data labeler or annotator. We evaluate the zero-shot performance of ChatGPT in the task of human-written vs. AI-generated text detection, and perform experiments on publicly available datasets. We empirically investigate if ChatGPT is symmetrically effective in detecting AI-generated or human-written text. Our findings provide insight on how ChatGPT and similar LLMs may be leveraged in automated detection pipelines by simply focusing on solving a specific aspect of the problem and deriving the rest from that solution. All code and data is available at \url{https://github.com/AmritaBh/ChatGPT-as-Detector}.

研究中探讨了ChatGPT作为AI生成文本检测器的性能，通过评估它在人工编写与AI生成文本检测任务上的零样本表现，并对公开可用的数据集进行实验。结果发现ChatGPT以及类似的大型语言模型可在自动化检测流程中发挥作用，通过专注于解决问题的一个特定方面并从该解决方案派生出其他方面的解决方案。

以火攻火：ChatGPT能否检测生成的人工智能文本？