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

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arxiv 2304.12008 v2 pith:AVTSXHGH submitted 2023-04-24 cs.CL

Peipeng Yu , Jiahan Chen , Xuan Feng , Zhihua Xia This is my paper
classification cs.CL
keywords chatgpt-writtendatasetdetectionabstractsacademicalgorithmschatgptcheat
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 3 Pith papers

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