  The development and popularization of large language models (LLMs) have
raised concerns that they will be used to create tailor-made, convincing
arguments to push false or misleading narratives online. Early work has found
that language models can generate content perceived as at least on par and
often more persuasive than human-written messages. However, there is still
limited knowledge about LLMs' persuasive capabilities in direct conversations
with human counterparts and how personalization can improve their performance.
In this pre-registered study, we analyze the effect of AI-driven persuasion in
a controlled, harmless setting. We create a web-based platform where
participants engage in short, multiple-round debates with a live opponent. Each
participant is randomly assigned to one of four treatment conditions,
corresponding to a two-by-two factorial design: (1) Games are either played
between two humans or between a human and an LLM; (2) Personalization might or
might not be enabled, granting one of the two players access to basic
sociodemographic information about their opponent. We found that participants
who debated GPT-4 with access to their personal information had 81.7% (p <
0.01; N=820 unique participants) higher odds of increased agreement with their
opponents compared to participants who debated humans. Without personalization,
GPT-4 still outperforms humans, but the effect is lower and statistically
non-significant (p=0.31). Overall, our results suggest that concerns around
personalization are meaningful and have important implications for the
governance of social media and the design of new online environments.
