Evaluating the Capabilities of LLMs for Persuasive Dialogue

Jordan Robinson; Angus R. Williams; Katie Atkinson; Anthony G. Cohn

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arXiv · 2608.29738

Evaluating the Capabilities of LLMs for Persuasive Dialogue

Jordan Robinson·Angus R. Williams·Katie Atkinson·Anthony G. Cohn

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Abstract

Large language models (LLMs) can generate apparently highly persuasive text, but does sounding persuasive mean arguing well? We introduce \textsc{Persuasio}, a multi-agent dialogue platform grounded in a formal argumentation-based theory of persuasion dialogues that adjudicates logical winners during free-text debates. Using this system, we generated 192 debates on a UK political topic between humans and LLMs, and evaluated 22 interlocutors through both automated adjudication and 9,702 crowdsourced pairwise judgements across 1{,}386 annotation instances. We observed a consistent decoupling between subjective and formal persuasiveness: LLMs dominated the subjective ranking yet performed substantially worse under argumentation-theoretic adjudication, where humans remained competitive. Multi-agent and retrieval-augmented variants further widened this divergence. These findings reveal a systematic gap between rhetorical fluency and formal argumentative strength in LLM-based persuasive dialogues.

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Jordan Robinson, Angus R. Williams, Katie Atkinson, Anthony G. Cohn. 2026-08-30. Evaluating the Capabilities of LLMs for Persuasive Dialogue. https://arxiv.org/abs/2608.29738

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