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
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。