CHEAT: A Large-Scale Dataset for Detecting CHatGPT-writtEn AbsTracts
Peipeng Yu, Jiahan Chen, Xuan Feng, Zhihua Xia
Abstract
The powerful ability of ChatGPT has caused widespread concern in the academic community. Malicious users could synthesize dummy academic content through ChatGPT, which is extremely harmful to academic rigor and originality. The need to develop ChatGPT-written content detection algorithms calls for large-scale datasets. In this paper, we initially investigate the possible negative impact of ChatGPT on academia, and present a large-scale CHatGPT-writtEn AbsTract dataset (CHEAT) to support the development of detection algorithms. In particular, the ChatGPT-written abstract dataset contains 35,304 synthetic abstracts, with <inline-formula><tex-math notation="LaTeX">$Generation$</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$Polish$</tex-math></inline-formula>, and <inline-formula><tex-math notation="LaTeX">$Fusion$</tex-math></inline-formula> as prominent representatives. Based on these data, we perform a thorough analysis of the existing text synthesis detection algorithms. We show that ChatGPT-written abstracts are detectable with well-trained detectors, while the detection difficulty increases with more human guidance involved.