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 ELABORA-TION 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.

Michele Joshua Maggini, Søren Fomsgaard, Michele Maestroni, Gaël Dias, Pablo Gamallo. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.