Discourse Realization of Generics in Human and LLM-generated Texts

ACL ARR 2026 January Submission10574 Authors

Keywords: coherence, discourse relations, misinformation, generics, persuasion

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 discourse even in highly generic texts enables them to "camouflage" argumentative texts as informative, enhancing their perceived credibility and persuasiveness.

Paper Type: Long

Research Area: Discourse, Pragmatics, and Reasoning

Research Area Keywords: coherence, discourse relations, discourse parsing

Contribution Types: Data resources, Data analysis, Position papers, Theory

Languages Studied: English

Submission Number: 10574