CHEAT: A Large-scale Dataset for Detecting ChatGPT-writtEn AbsTracts
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866911783273365504 |
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| author | Yu, Peipeng Chen, Jiahan Feng, Xuan Xia, Zhihua |
| author_facet | Yu, Peipeng Chen, Jiahan Feng, Xuan Xia, Zhihua |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_12008 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | CHEAT: A Large-scale Dataset for Detecting ChatGPT-writtEn AbsTracts Yu, Peipeng Chen, Jiahan Feng, Xuan Xia, Zhihua Computation and Language 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. |
| title | CHEAT: A Large-scale Dataset for Detecting ChatGPT-writtEn AbsTracts |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2304.12008 |