Evaluating the Capabilities of LLMs for Persuasive Dialogue

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.

Computation and Language (cs.CL)

arXiv:2608.29738 [cs.CL]

(or arXiv:2608.29738v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.29738 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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