Large Language Models (LLMs) can generate texts that carry the risk of
various misuses, including plagiarism, planting fake reviews on e-commerce
platforms, or creating fake social media postings that can sway election
results. Detecting whether a text is machine-generated has thus become
increasingly important. While machine-learning-based detection strategies
exhibit superior performance, they often lack generalizability, limiting their
practicality. In this work, we introduce GPT Paternity Test (GPT-Pat), which
reliably detects machine-generated text across varied datasets. Given a text
under scrutiny, we leverage ChatGPT to generate a corresponding question and
provide a re-answer to the question. By comparing the similarity between the
original text and the generated re-answered text, it can be determined whether
the text is machine-generated. GPT-Pat consists of a Siamese network to compute
the similarity between the original text and the generated re-answered text and
a binary classifier. Our method achieved an average accuracy of 94.57% on four
generalization test sets, surpassing the state-of-the-art RoBERTa-based method
by 12.34%. The accuracy drop of our method is only about half of that of the
RoBERTa-based method when it is attacked by re-translation and polishing.

该研究提出了一种名为 GPT Paternity Test（GPT-Pat）的新方法，利用对话生成技术来检测文本是否是机器生成的，并使用孪生网络作为计算机制以及一个二进制分类器。经过实验，该方法在四个广义测试集中的平均准确率达到了 94.57％，并表现出比 RoBERTa 方法高出 12.34％的性能。