面向语言模型的战略说服
Towards Strategic Persuasion with Language Models
AI总结：
本文基于贝叶斯说服理论，利用人类说服数据集构建环境，通过强化学习训练语言模型进行战略说服，验证了LLMs在不同规模下的说服能力
摘要：
大型语言模型（LLMs）已展现出与人类相当的说服能力，提供了有前景的益处，但也引发了社会担忧。然而，系统评估LLMs的说服能力本质上具有挑战性，因为人类在不同领域中的说服效果差异显著。在本文中，我们采取理论驱动的方法，提供一个可扩展且原理明确的框架来研究LLMs的说服能力。基于贝叶斯说服理论，我们将人类-人类说服数据集重新利用，构建用于评估和训练LLMs作为战略说服者的环境。我们的结果表明，前沿模型能够持续实现高说服收益，并展现出与理论特征相一致的复杂说服策略。在此基础上，我们使用强化学习在我们的环境中训练LLMs进行战略说服。我们的结果还表明，即使小型LLMs也能通过强化学习获得显著更高的说服收益。
英文摘要：
Large language models (LLMs) have demonstrated strong persuasive capabilities comparable to those of humans, offering promising benefits while raising societal concerns. However, systematically evaluating the persuasive capabilities of LLMs is inherently challenging, as the effectiveness of persuasion among humans varies significantly across different domains. In this paper, we take a theory-driven approach to provide a scalable and principled framework for studying the persuasive capabilities of LLMs. Grounded in Bayesian persuasion theory, we repurpose human-human persuasion datasets to construct environments for evaluating and training LLMs as strategic persuaders. Our results reveal that frontier models can consistently achieve high persuasion gains and exhibit sophisticated persuasion strategies that align with theoretical characterizations. Building on this, we use reinforcement learning to train LLMs for strategic persuasion in our environments. Our results also demonstrate that even small LLMs can obtain significantly higher persuasion gains through reinforcement learning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
