The increasing capability of Large Language Models to act as human-like social agents raises two important questions in the area of opinion dynamics. First, whether these agents can generate effective arguments that could be injected into the online discourse to steer the public opinion. Second, whether artificial agents can interact with each other to reproduce dynamics of persuasion typical of human social systems, opening up opportunities for studying synthetic social systems as faithful proxies for opinion dynamics in human populations. To address these questions, we designed a synthetic persuasion dialogue scenario on the topic of climate change, where a 'convincer' agent generates a persuasive argument for a 'skeptic' agent, who subsequently assesses whether the argument changed its internal opinion state. Different types of arguments were generated to incorporate different linguistic dimensions underpinning psycho-linguistic theories of opinion change. We then asked human judges to evaluate the persuasiveness of machine-generated arguments. Arguments that included factual knowledge, markers of trust, expressions of support, and conveyed status were deemed most effective according to both humans and agents, with humans reporting a marked preference for knowledge-based arguments. Our experimental framework lays the groundwork for future in-silico studies of opinion dynamics, and our findings suggest that artificial agents have the potential of playing an important role in collective processes of opinion formation in online social media.

翻译：随着大型语言模型作为类人社交代理的能力日益增强，这在观点动力学领域引发了两个重要问题。首先，这些代理能否生成有效论点，并将其注入在线讨论以引导公众舆论。其次，人工智能代理之间能否相互交互，再现人类社会系统中典型的说服动力学，从而为研究合成社会系统作为人类群体观点动力学的可靠代理开辟机遇。为探讨这些问题，我们设计了一个关于气候变化的合成说服对话场景：一个"说服者"代理为"怀疑者"代理生成具有说服力的论点，随后怀疑者评估该论点是否改变了其内部观点状态。我们生成了不同类型的论点，以融入支撑观点改变的心理语言学理论所涉及的不同语言维度。随后，我们请人类评判员评估机器生成论点的说服力。根据人类和代理的评估，包含事实知识、信任标记、支持表达和地位传递的论点被认为最有效，其中人类特别偏好基于知识的论点。我们的实验框架为未来观点动力学的计算机模拟研究奠定了基础，研究结果表明，人工智能代理有潜力在在线社交媒体的集体观点形成过程中发挥重要作用。