Argument Collapse: LLMs Flatten Long-Form Public Debate

A new study finds that large language models (LLMs) cause 'argument collapse' in public debate, with only 3.4% of LLM-generated main arguments being unique within a debate compared to 65.3% for humans. Analyzing over 23,000 LLM essays alongside human-written responses from the New York Times and Boston Review, researchers show that LLMs reuse generic sub-arguments and follow rigid structures, potentially flattening long-form discourse.

Computer Science Computation and Language Submitted on 1 Jun 2026 v1 https://arxiv.org/abs/2606.01736v1 , last revised 5 Jun 2026 this version, v3 Title:Argument Collapse: LLMs Flatten Long-Form Public Debate View PDF /pdf/2606.01736 HTML experimental https://arxiv.org/html/2606.01736v3 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 arguments are unique within a debate, compared to 3.4% of LLM main arguments. Asking LLMs to generate diverse answers adds variation, but a typical model recovers only about half of the distinct human main arguments, with much of the added variation falling outside the observed human argument space. Collapse also appears in sub-arguments, where among essays with the same main argument, 41.0% of human sub-arguments are unique versus 9.1% from LLM responses. Qualitatively, LLMs often reuse generalized and hedged sub-arguments, while humans prefer more concrete and topic-specific ones. Structure-wise, LLM-generated essays tend to follow a more fixed arc, often opening with a direct claim and moving quickly toward proposals. The same patterns hold in longer BR essays, suggesting that argument collapse extends beyond short-form responses. Submission history From: Yekyung Kim view email /show-email/75c4ff03/2606.01736 Mon, 1 Jun 2026 05:58:50 UTC 9,329 KB v1 /abs/2606.01736v1 Wed, 3 Jun 2026 21:13:44 UTC 4,203 KB v2 /abs/2606.01736v2 v3 Fri, 5 Jun 2026 20:16:04 UTC 4,189 KB References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer What is the Explorer? https://info.arxiv.org/labs/showcase.html arxiv-bibliographic-explorer Connected Papers What is Connected Papers? https://www.connectedpapers.com/about Litmaps What is Litmaps? https://www.litmaps.co/ scite Smart Citations What are Smart Citations? https://www.scite.ai/ Code, Data and Media Associated with this Article alphaXiv What is alphaXiv? https://alphaxiv.org/ CatalyzeX Code Finder for Papers What is CatalyzeX? https://www.catalyzex.com DagsHub What is DagsHub? https://dagshub.com/ Gotit.pub What is GotitPub? http://gotit.pub/faq Hugging Face What is Huggingface? https://huggingface.co/huggingface ScienceCast What is ScienceCast? https://sciencecast.org/welcome Demos Recommenders and Search Tools Influence Flower What are Influence Flowers? https://influencemap.cmlab.dev/ CORE Recommender What is CORE? https://core.ac.uk/services/recommender arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs https://info.arxiv.org/labs/index.html .