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.

研究了 ChatGPT 对学术界原创性和严谨性的可能负面影响，并提出了一个大规模数据集以支持检测算法的开发。该数据集包含了 35,304 个合成摘要，同时对现有的文本合成检测算法进行了深入分析，表明 ChatGPT 合成的摘要是可以被检测出来的，但随着人类参与的增加，检测难度会增加。