Detecting ChatGPT: A Survey of the State of Detecting ChatGPT-Generated Text

Mahdi Dhaini, Wessel Poelman, Ege Erdogan
9/14/2023
cs.CLcs.AI

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Abstract

While recent advancements in the capabilities and widespread accessibility of generative language models, such as ChatGPT (OpenAI, 2022), have brought about various benefits by generating fluent human-like text, the task of distinguishing between human- and large language model (LLM) generated text has emerged as a crucial problem. These models can potentially deceive by generating artificial text that appears to be human-generated. This issue is particularly significant in domains such as law, education, and science, where ensuring the integrity of text is of the utmost importance. This survey provides an overview of the current approaches employed to differentiate between texts generated by humans and ChatGPT. We present an account of the different datasets constructed for detecting ChatGPT-generated text, the various methods utilized, what qualitative analyses into the characteristics of human versus ChatGPT-generated text have been performed, and finally, summarize our findings into general insights

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Cite this paper

@article{dhaini2023detecting,
  title  = {Detecting ChatGPT: A Survey of the State of Detecting ChatGPT-Generated Text},
  author = {Mahdi Dhaini and Wessel Poelman and Ege Erdogan},
  year   = {2023},
  eprint = {2309.07689},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2309.07689v1}
}

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