Recent advances in large language models (LLM) have enabled richer social
simulations, allowing for the study of various social phenomena with LLM-based
agents. However, most work has used an omniscient perspective on these
simulations (e.g., single LLM to generate all interlocutors), which is
fundamentally at odds with the non-omniscient, information asymmetric
interactions that humans have. To examine these differences, we develop an
evaluation framework to simulate social interactions with LLMs in various
settings (omniscient, non-omniscient). Our experiments show that interlocutors
simulated omnisciently are much more successful at accomplishing social goals
compared to non-omniscient agents, despite the latter being the more realistic
setting. Furthermore, we demonstrate that learning from omniscient simulations
improves the apparent naturalness of interactions but scarcely enhances goal
achievement in cooperative scenarios. Our findings indicate that addressing
information asymmetry remains a fundamental challenge for LLM-based agents.

通过模拟社交交互并对比全知与非全知的情况，我们的研究发现，尽管非全知模拟更贴近真实情景，但全知模拟所生成的角色在实现社交目标上更加成功。此外，我们证明从全知模拟中学习可以提高交互的表现自然度，但在合作情境中对目标的实现帮助有限。研究结果指出解决语言模型中的信息不对称仍然是一个基本的挑战。