Authors: Z Cheng, J You
Year: 2025
Published in: arXiv preprint arXiv:2509.22989, 2025 - arxiv.org
Institution: University of Southern California, University of California Berkeley
Research Area: Artificial Intelligence, Computers and Society, Computer Science and Game Theory, Strategic Persuasion, Reinforcement Learning, Language Models, LLM, RLHF
Discipline: Artificial Intelligence
This paper introduces a scalable framework, utilizing Bayesian Persuasion, to evaluate and train LLMs for strategic persuasion, demonstrating significant persuasion gains and effective strategies through reinforcement learning.
Methods: Repurposed human-human persuasion datasets for evaluation and training; applied Bayesian Persuasion framework; used reinforcement learning to optimize LLMs for strategic persuasion.
Key Findings: The persuasive capabilities and strategies of large language models (LLMs) in various settings.
Citations: 1