Large Language Models (LLMs) have shown remarkable promise in their ability to interact proficiently with humans. Subsequently, their potential use as artificial confederates and surrogates in sociological experiments involving conversation is an exciting prospect. But how viable is this idea? This paper endeavors to test the limits of current-day LLMs with a pre-registered study integrating real people with LLM agents acting as people. The study focuses on debate-based opinion consensus formation in three environments: humans only, agents and humans, and agents only. Our goal is to understand how LLM agents influence humans, and how capable they are in debating like humans. We find that LLMs can blend in and facilitate human productivity but are less convincing in debate, with their behavior ultimately deviating from human's. We elucidate these primary failings and anticipate that LLMs must evolve further before being viable debaters.

翻译：大型语言模型（LLMs）在与人类熟练交互方面展现出卓越潜力。因此，将其作为人工同谋者和替代者，应用于涉及对话的社会学实验这一前景令人振奋。但这一设想可行性如何？本文通过一项预先注册的研究，将真实参与者与扮演人类角色的LLM智能体相结合，致力于检验当前LLMs的能力边界。该研究聚焦于三种环境下的基于辩论的意见共识形成：纯人类组、人类与智能体混合组、纯智能体组。我们的目标是理解LLM智能体如何影响人类，以及它们在模仿人类辩论方面的能力。研究发现，LLMs能够融入人类群体并提升工作效率，但在辩论中说服力较弱，其行为最终与人类存在偏差。我们阐明了这些主要缺陷，并预测LLMs在成为可信赖的辩论者之前仍需进一步进化。