arXiv:2606.01736v1 Announce Type: new Abstract: As LLMs are increasingly used to draft public-facing arguments, they may flatten public debate by repeatedly introducing the same polished, plausible arguments. We study argument collapse, the tendency of essays generated by different LLMs to converge to a smaller set of main arguments, sub-arguments, and paragraph-level structures. We compare 1,039 human responses from 195 New York Times (NYT) debates, 448 human responses from 61 longer-form Boston Review (BR) forums, and 23,384 LLM-generated essays. In the NYT corpus, 65.3% of human main argume
The increasing deployment of LLMs for content generation, particularly in public debate, makes the study of their homogenizing effects particularly salient at this moment.
This research provides empirical evidence of how AI-generated content can reduce diversity of thought in public discourse, impacting societal consensus formation and the quality of debate.
The understanding of LLMs' impact on information ecosystems shifts from speculative to empirically grounded, highlighting a new challenge for critical thinking and democratic processes.
- · Platforms promoting diverse human-generated content
- · Creators of novel or nuanced arguments
- · Critical media literacy tools
- · Homogenized LLM-generated content providers
- · Public forums relying solely on LLM-generated debate
- · Diversity of opinion in public discourse
Public debates become less diverse, with LLM-generated arguments converging on common themes.
Societies face an increased challenge in discerning original thought from polished, repetitive AI outputs, potentially leading to 'groupthink' or echo chambers.
The perceived value of human-generated, diverse perspectives increases, prompting a reassessment of AI's role in opinion formation and public discourse.
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