@inproceedings{fomsgaard-etal-2026-discourse,
    title = "Discourse Realization of Generics in Human and {LLM}-generated Texts",
    author = "Fomsgaard, S{\{}{\textbackslash}o{\}}ren Kirkegaard  and
      Pastor, Martial  and
      Dias, Ga{\textbackslash}{''}el  and
      Oostdijk, Nelleke",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1521/",
    pages = "32938--32960",
    ISBN = "979-8-89176-390-6",
    abstract = "Large Language Models (LLMs) often produce texts that appear coherent and credible, even when their factual reliability is uncertain. This paper investigates whether such perceived credibility correlates with the pervasive use of generics{---}generalizations without explicit quantification. We introduce a text-level genericity score derived from clause-level annotations and apply it to argumentative essays produced by humans and LLMs. To analyze how generics are realized in discourse, we employ Rhetorical Structure Theory to examine coherence relations across varying levels of genericity. Results show that according to our genericity metric, human texts are less generic than LLM-produced texts. As regards discourse, higher genericity correlates with less structured, paratactic structures, while for some models coherence is maintained through elaboration relations. Our findings suggest that some LLMs maintain well-structured coherence even in highly generic texts, which might enable them to ``camouflage'' argumentative texts as informative, enhancing their perceived credibility and persuasiveness."
}