  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 call 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 Generation, Polish, and Mix 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,
while the detection difficulty increases with human involvement.

Computation and Language