CHEAT: A Large-scale Dataset for Detecting ChatGPT-writtEn AbsTracts

Peipeng Yu, Jiahan Chen, Xuan Feng, Zhihua Xia
4/24/2023
cs.CL

Official code: botianzhe/CHEAT (22 stars, MIT, last commit 2025-04-26).

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 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.Our dataset is available in https://github.com/botianzhe/CHEAT.

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Code Implementations(1)

botianzhe/CHEATOfficial100%
MIT
228May 22, 20231 years agoMIT

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Cite this paper

@article{yu2023cheat,
  title  = {CHEAT: A Large-scale Dataset for Detecting ChatGPT-writtEn AbsTracts},
  author = {Peipeng Yu and Jiahan Chen and Xuan Feng and Zhihua Xia},
  year   = {2023},
  eprint = {2304.12008},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2304.12008v2}
}

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