Detecting ChatGPT: A Survey of the State of Detecting ChatGPT-Generated
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Mahdi Dhaini Wessel Poelman Ege Erdogan
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