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.

We explore the use of discourse parsers for extracting a particular discourse structure in a real-world social media scenario. Specifically, we focus on enhancing parser performance through the integration of synthetic data generated by large language models (LLMs). We conduct experiments using a newly developed dataset of 1,170 local RST discourse structures, including 900 synthetic and 270 gold examples, covering three social media platforms: online news comments sections, a discussion forum (Reddit), and a social media messaging platform (Twitter). Our primary goal is to assess the impact of LLM-generated synthetic training data on parser performance in a raw text setting without pre-identified discourse units. While both top-down and bottom-up RST architectures greatly benefit from synthetic data, challenges remain in classifying evaluative discourse structures.