使用大语言模型的说服博弈
Persuasion Games using Large Language Models
AI总结：
本文提出多智能体协作框架，通过主智能体说服对话与辅助智能体信息检索、策略制定及抵抗-说服映射，提升LLM在保险、银行和零售等领域影响用户决策的能力。
摘要：
大语言模型（LLMs）已成为能够理解并生成类人文本的强大工具。本文探讨了LLMs在塑造用户观点并进而影响其在特定任务上的决策方面的潜力。这种能力在投资、信用卡和保险等多个领域都有应用，在这些领域中，它们协助用户选择合适的保险政策、投资计划、信用卡、零售方案，以及行为改变支持系统（BCSS）。我们提出了一个精细的多智能体框架，其中一组智能体以协作方式运作。主智能体通过说服性对话直接与用户智能体互动，而辅助智能体则执行信息检索、响应分析、说服策略制定和事实验证等任务。我们实验中的经验证据表明，这种协作方法显著增强了LLM的说服效力。我们持续分析用户智能体对说服努力的抵抗，并通过结合基于规则和基于LLM的抵抗-说服映射技术来加以应对。我们采用模拟角色，并在保险、银行和零售领域生成对话，以评估大语言模型（LLMs）在识别、适应和影响不同人格类型方面的能力。同时，我们考察了LLM模拟角色所采用的抵抗机制。说服效果通过互动前后的可量化调查、LLM对对话生成的评分以及用户决策（购买或不购买）来量化。
英文摘要：
Large Language Models (LLMs) have emerged as formidable instruments capable of comprehending and producing human-like text. This paper explores the potential of LLMs, to shape user perspectives and subsequently influence their decisions on particular tasks. This capability finds applications in diverse domains such as Investment, Credit cards and Insurance, wherein they assist users in selecting appropriate insurance policies, investment plans, Credit cards, Retail, as well as in Behavioral Change Support Systems (BCSS). We present a sophisticated multi-agent framework wherein a consortium of agents operate in collaborative manner. The primary agent engages directly with user agents through persuasive dialogue, while the auxiliary agents perform tasks such as information retrieval, response analysis, development of persuasion strategies, and validation of facts. Empirical evidence from our experiments demonstrates that this collaborative methodology significantly enhances the persuasive efficacy of the LLM. We continuously analyze the resistance of the user agent to persuasive efforts and counteract it by employing a combination of rule-based and LLM-based resistance-persuasion mapping techniques. We employ simulated personas and generate conversations in insurance, banking, and retail domains to evaluate the proficiency of large language models (LLMs) in recognizing, adjusting to, and influencing various personality types. Concurrently, we examine the resistance mechanisms employed by LLM simulated personas. Persuasion is quantified via measurable surveys before and after interaction, LLM-generated scores on conversation, and user decisions (purchase or non-purchase).
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。
