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Do LLMs Change Their Minds Like Humans? Diagnosing Human--LLM Divergence in Single-Turn Persuasion Judgments — arXiv:2608.29803

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.29803
License (as archived): CC0-1.0
Archived: 2026-10-17T08:08:02.425Z
SHA-256 of the archived text: 5cb60b14695ea67299ce140e347bc4969d1e518c4b7bd2a904cbd5596485473e
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How to cite

Do LLMs Change Their Minds Like Humans? Diagnosing Human--LLM Divergence in Single-Turn Persuasion Judgments — arXiv:2608.29803 — arXiv (en), https://arxiv.org/abs/2608.29803 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_dbf2e446dc41 (Metraipolis archive dc_dbf2e446dc41, sha256 5cb60b14695ea67299ce140e347bc4969d1e518c4b7bd2a904cbd5596485473e)

BibTeX

@misc{dcdbf2e446dc41,
  title = {Do LLMs Change Their Minds Like Humans? Diagnosing Human--LLM Divergence in Single-Turn Persuasion Judgments — arXiv:2608.29803},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.29803},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_dbf2e446dc41; sha256 5cb60b14695ea67299ce140e347bc4969d1e518c4b7bd2a904cbd5596485473e},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.29803. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Large language models (LLMs) are increasingly deployed as proxies for human participants in social simulations, yet whether they update their beliefs in response to persuasive arguments, as humans do, remains poorly understood. We conduct a systematic comparison using a naturally occurring online persuasion corpus in which original posters explicitly verify whether a reply changed their view. Our results show that LLMs achieve only slight agreement with humans (Cohen's kappa ranging from 0.079 to 0.178). Content-level analyses show that humans and LLMs agree on the strongest persuasion cues but diverge on finer ones: humans are more swayed by novel content and assertive language, whereas LLMs favor topical similarity and surface-level formatting. At the level of persuasion strategy, LLMs underweight emotional appeals and overweight credibility signals relative to humans, while the type of proposition under debate exerts no measurable effect on the degree of divergence. Furthermore, switching from first-person role-playing to third-person observation shifts all models toward greater resistance to persuasion, with the effect varying across persuasion strategies and textual features. These findings highlight the risk of treating LLM judgments as faithful proxies for human belief updating and point to structural differences in how LLMs and humans process persuasive discourse. Our code is available at this https URL .

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-01 in cs.CL).
Categorie arXiv: Computers and Society (cs.CY) ; Computation and Language (cs.CL).
Pagina del lavoro: https://arxiv.org/abs/2608.29803 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T07:47:52.911Z dalla pagina https://arxiv.org/catchup/cs.CL/2026-09-01?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).