@inproceedings{alavoine-etal-2024-limitations,
    title = "Limitations of Human Identification of Automatically Generated Text",
    author = "Alavoine, Nad{\`e}ge  and
      Coavoux, Maximin  and
      Esperan{\c{c}}a-Rodier, Emmanuelle  and
      Gallienne, Romane  and
      Gonz{\'a}lez-Gallardo, Carlos-Emiliano  and
      Goulian, J{\'e}r{\^o}me  and
      Moreno, Jose G.  and
      N{\'e}v{\'e}ol, Aur{\'e}lie  and
      Schwab, Didier  and
      Segonne, Vincent  and
      Simoens, Johanna",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.919",
    pages = "10511--10516",
    abstract = "Neural text generation is receiving broad attention with the publication of new tools such as ChatGPT. The main reason for that is that the achieved quality of the generated text may be attributed to a human writer by the naked eye of a human evaluator. In this paper, we propose a new corpus in French and English for the task of recognising automatically generated texts and we conduct a study of how humans perceive the text. Our results show, as previous work before the ChatGPT era, that the generated texts by tools such as ChatGPT share some common characteristics but they are not clearly identifiable which generates different perceptions of these texts.",
}

Neural text generation is receiving broad attention with the publication of new tools such as ChatGPT. The main reason for that is that the achieved quality of the generated text may be attributed to a human writer by the naked eye of a human evaluator. In this paper, we propose a new corpus in French and English for the task of recognising automatically generated texts and we conduct a study of how humans perceive the text. Our results show, as previous work before the ChatGPT era, that the generated texts by tools such as ChatGPT share some common characteristics but they are not clearly identifiable which generates different perceptions of these texts.
Alavoine, Nad{\`e}ge Coavoux, Maximin Esperan{\c{c}}a-Rodier, Emmanuelle Gallienne, Romane Gonz{\'a}lez-Gallardo, Carlos-Emiliano Goulian, J{\'e}r{\^o}me Moreno, Jose G. N{\'e}v{\'e}ol, Aur{\'e}lie Schwab, Didier Segonne, Vincent Simoens, Johanna
Limitations of Human Identification of Automatically Generated Text