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Large Language Models (LLMs) can generate texts that carry the risk ofvarious misuses, including plagiarism, planting fake reviews on e-commerceplatforms, or creating fake social media postings that can sway electionresults. Detecting whether a text is machine-generated has thus becomeincreasingly important. While machine-learning-based detection strategiesexhibit superior performance, they often lack generalizability, limiting theirpracticality. In this work, we introduce GPT Paternity Test (GPT-Pat), whichreliably detects machine-generated text across varied datasets. Given a textunder scrutiny, we leverage ChatGPT to generate a corresponding question andprovide a re-answer to the question. By comparing the similarity between theoriginal text and the generated re-answered text, it can be determined whetherthe text is machine-generated. GPT-Pat consists of a Siamese network to computethe similarity between the original text and the generated re-answered text anda binary classifier. Our method achieved an average accuracy of 94.57% on fourgeneralization test sets, surpassing the state-of-the-art RoBERTa-based methodby 12.34%. The accuracy drop of our method is only about half of that of theRoBERTa-based method when it is attacked by re-translation and polishing.

GPT Paternity Test: GPT Generated Text Detection with GPT Genetic Inheritance