@inproceedings{dhaini-etal-2023-detecting,
    title = "Detecting {C}hat{GPT}: A Survey of the State of Detecting {C}hat{GPT}-Generated Text",
    author = "Dhaini, Mahdi  and
      Poelman, Wessel  and
      Erdogan, Ege",
    editor = "Hardalov, Momchil  and
      Kancheva, Zara  and
      Velichkov, Boris  and
      Nikolova-Koleva, Ivelina  and
      Slavcheva, Milena",
    booktitle = "Proceedings of the 8th Student Research Workshop associated with the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2023",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://aclanthology.org/2023.ranlp-stud.1",
    pages = "1--12",
    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.",
}