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The Thin Line Between Comprehension and Persuasion in LLMs

Paper · 2026 · Adrian de Wynter and Tangming Yuan

Summary

LLMs can sustain fluent, convincing conversations, but does that persuasiveness come with a real grasp of the discussion? The authors test this through informal debates. They collected transcripts of three kinds: humans arguing against plain chat LLMs, humans arguing against LLMs equipped with a formal dialogue model (FDM), and LLMs debating each other. The FDM was an adjusted version of the educational DE model. The authors tracked how participants' beliefs shifted and how they rated the experience. Five human annotators scored every transcript at the premise, argument and whole-debate levels. LLM judges then scored the same material, and their scores were compared with the human ones. Ablations varied the FDM, the modality (text vs. audio) and whether AI involvement was disclosed.

LLMs proved capable debaters, especially with the FDM, and changed people's minds. Audiences grew more sceptical once AI was suspected or revealed. As evaluators, however, LLMs agreed with humans only at about chance level. The gap between persuading and understanding matters wherever LLM explanations must be trustworthy.

Key points

  • Dataset: 984 utterances on 51 topics. It covers 10 human vs. LLM \ FDM debates, 28 human vs. LLM + FDM debates and 13 LLM–LLM debates. Human participants picked topics they felt strongly about.
  • FDM: The DE model (Yuan et al., 2008) derives from Mackenzie's DC model. The authors modified it to add concession rules and to cap how many questions the LLM could ask.
  • Annotation scheme: Criteria C-0 to C-7 cover reasons, argument convincingness and strength, and the debate winner. Human agreement (Cohen's weighted κ) was 0.86 on the argument criteria and 0.41 on choosing the winner.
  • LLM judges: Their agreement with human annotators was close to chance. Their own argument-strength scores did not predict which side they declared the winner.
  • Open resources: Both open- and closed-source models were tested, including GPT-4o, which has since been retired. Code and data are released under an MIT licence at github.com/adewynter/the_thin_line.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksPhilosophy · 284 works

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 3,320 works

Cite

Cite as Adrian de Wynter and Tangming Yuan. 2026. The Thin Line Between Comprehension and Persuasion in LLMs. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-acl.329

@inproceedings{wynter2026thin,
  title = {{The Thin Line Between Comprehension and Persuasion in LLMs}},
  author = {Adrian {de Wynter} and Tangming Yuan},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.329},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.329}
}

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