When Large Language Models are More PersuasiveThan Incentivized Humans, and Why

Jiacheng Liu; Francesco Salvi; Philipp Schoenegger; Xiaoli Nan; Ramit Debnath; Barbara Fasolo; Evelina Leivada; Gabriel Recchia; Fritz Günther; Ali Zarifhonarvar; Joe Kwon; Zahoor Ul Islam; Marco Dehnert; Daryl Y. H. Lee; Madeline G. Reinecke; David G. Kamper; Mert Kobaş; Adam Sandford; Jonas Kgomo; Luke Hewitt; Shreya Kapoor; Kerem Oktar; Eyup Engin Kucuk; Bo Feng; Cameron R. Jones; Izzy Gainsburg; Sebastian Olschewski; Nora Heinzelmann; Francisco Cruz; Ben M. Tappin; Tao Ma; Peter S. Park; Rayan Onyonka; Arthur Hjorth; Peter Slattery; Qingcheng Zeng; Lennart Finke; Igor Grossmann; Alessandro Salatiello; Ezra Karger

arXiv · 2505.09662

When Large Language Models are More PersuasiveThan Incentivized Humans, and Why

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Abstract

Large Language Models (LLMs) have been shown to be highly persuasive, but when and why they outperform humans is still an open question. We compare the persuasiveness of two LLMs (Claude 3.5 Sonnet and DeepSeek v3) against humans who had incentives to persuade, using an interactive, real-time conversational setting. We demonstrate that LLMs persuasive superiority is context-dependent: it depends on whether the persuasion attempt is truthful (towards the right answer) or deceptive (towards the wrong answer) and on the LLM model, and wanes over repeated interactions (unlike human persuasiveness). In our first large-scale experiment, humans vs LLMs (Claude 3.5 Sonnet) interacted with other humans who were completing an online quiz for a reward, attempting to persuade them toward a given (either correct or incorrect) answer. Claude was more persuasive than incentivized human persuaders both in truthful and deceptive contexts and it significantly increased accuracy if persuasion was truthful, but decreased it if persuasion was deceptive. In a follow-up experiment with Deepseek v3, we replicated the findings about accuracy but found greater LLM persuasiveness only if the persuasion was deceptive. Linguistic analyses of the persuaders texts suggest that these effects may be due to LLMs expressing higher conviction than humans.

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Jiacheng Liu, Francesco Salvi, Philipp Schoenegger, Xiaoli Nan, Ramit Debnath, Barbara Fasolo, Evelina Leivada, Gabriel Recchia, Fritz Günther, Ali Zarifhonarvar, Joe Kwon, Zahoor Ul Islam, Marco Dehnert, Daryl Y. H. Lee, Madeline G. Reinecke, David G. Kamper, Mert Kobaş, Adam Sandford, Jonas Kgomo, Luke Hewitt, Shreya Kapoor, Kerem Oktar, Eyup Engin Kucuk, Bo Feng, Cameron R. Jones, Izzy Gainsburg, Sebastian Olschewski, Nora Heinzelmann, Francisco Cruz, Ben M. Tappin, Tao Ma, Peter S. Park, Rayan Onyonka, Arthur Hjorth, Peter Slattery, Qingcheng Zeng, Lennart Finke, Igor Grossmann, Alessandro Salatiello, Ezra Karger. 2026-08-13. When Large Language Models are More PersuasiveThan Incentivized Humans, and Why. https://arxiv.org/abs/2505.09662

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