@inproceedings{fomsgaard-etal-2026-discourse,
title = "Discourse Realization of Generics in Human and {LLM}-generated Texts",
author = {Fomsgaard, S{\o}ren Kirkegaard and
Pastor, Martial and
Dias, Ga{\"e}l 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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Discourse Realization of Generics in Human and LLM-generated Texts
%A Fomsgaard, Søren Kirkegaard
%A Pastor, Martial
%A Dias, Gaël
%A Oostdijk, Nelleke
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F fomsgaard-etal-2026-discourse
%X 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.
%U https://aclanthology.org/2026.acl-long.1521/
%P 32938-32960
Markdown (Informal)
[Discourse Realization of Generics in Human and LLM-generated Texts](https://aclanthology.org/2026.acl-long.1521/) (Fomsgaard et al., ACL 2026)
ACL
- Søren Kirkegaard Fomsgaard, Martial Pastor, Gaël Dias, and Nelleke Oostdijk. 2026. Discourse Realization of Generics in Human and LLM-generated Texts. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 32938–32960, San Diego, California, United States. Association for Computational Linguistics.