Generative Social Agents (GSAs) are increasingly impacting human users through persuasive means. On the one hand, they might motivate users to pursue personal goals, such as healthier lifestyles. On the other hand, they are associated with potential risks like manipulation and deception, which are induced by limited control over probabilistic agent outputs. However, as GSAs manifest communicative patterns based on available knowledge, their behavior may be regulated through their access to such knowledge. Following this approach, we explored persuasive ChatGPT-generated messages in the context of human-robot physiotherapy motivation. We did so by comparing ChatGPT-generated responses to predefined inputs from a hypothetical physiotherapy patient. In Study 1, we qualitatively analyzed 13 ChatGPT-generated dialogue scripts with varying knowledge configurations regarding persuasive message characteristics. In Study 2, third-party observers (N = 27) rated a selection of these dialogues in terms of the agent's expressiveness, assertiveness, and persuasiveness. Our findings indicate that LLM-based GSAs can adapt assertive and expressive personality traits -- significantly enhancing perceived persuasiveness. Moreover, persuasiveness significantly benefited from the availability of information about the patients' age and past profession, mediated by perceived assertiveness and expressiveness. Contextual knowledge about physiotherapy benefits did not significantly impact persuasiveness, possibly because the LLM had inherent knowledge about such benefits even without explicit prompting. Overall, the study highlights the importance of empirically studying behavioral patterns of GSAs, specifically in terms of what information generative AI systems require for consistent and responsible communication.

翻译：生成式社交智能体正日益通过说服性手段影响人类用户。一方面，它们可以激励用户追求个人目标，例如更健康的生活方式。另一方面，它们也存在操纵和欺骗等潜在风险，这些风险源于对概率性智能体输出的有限控制。然而，由于生成式社交智能体的沟通模式基于可用知识，其行为可通过知识获取途径进行调控。基于此思路，我们在人机物理治疗动机对话场景中探究了ChatGPT生成的说服性信息。我们通过比较ChatGPT对假设物理治疗患者预设输入所生成的回复展开研究。在研究1中，我们定性分析了13个具有不同知识配置的ChatGPT生成对话脚本，重点关注说服性信息的特征。在研究2中，第三方观察者（N = 27）从智能体的表达力、决断力和说服力三个维度对这些对话样本进行了评分。研究结果表明，基于大语言模型的生成式社交智能体能够适配决断型和表达型人格特质——显著提升感知说服力。此外，关于患者年龄和过往职业的信息可及性通过感知决断力和表达力的中介作用，显著增强了说服效果。而关于物理治疗益处的背景知识未对说服力产生显著影响，这可能是因为大语言模型即使在没有明确提示的情况下也具备此类固有知识。总体而言，本研究强调了实证研究生成式社交智能体行为模式的重要性，特别是在生成式人工智能系统需要何种信息才能实现一致且负责任的沟通方面。