Recent advancements in natural language processing, especially the emergence of Large Language Models (LLMs), have opened exciting possibilities for constructing computational simulations designed to replicate human behavior accurately. However, LLMs are complex statistical learners without straightforward deductive rules, making them prone to unexpected behaviors. In this study, we highlight the limitations of LLMs in simulating human interactions, particularly focusing on LLMs' ability to simulate political debates. Our findings indicate a tendency for LLM agents to conform to the model's inherent social biases despite being directed to debate from certain political perspectives. This tendency results in behavioral patterns that seem to deviate from well-established social dynamics among humans. We reinforce these observations using an automatic self-fine-tuning method, which enables us to manipulate the biases within the LLM and demonstrate that agents subsequently align with the altered biases. These results underscore the need for further research to develop methods that help agents overcome these biases, a critical step toward creating more realistic simulations.

翻译：近年来，自然语言处理领域的进展，特别是大型语言模型（LLMs）的出现，为构建旨在精确复制人类行为的计算模拟开辟了令人兴奋的可能性。然而，LLMs是复杂的统计学习器，缺乏直接的演绎规则，因此容易产生意外行为。在本研究中，我们揭示了LLMs在模拟人类互动方面的局限性，特别关注其模拟政治辩论的能力。我们的研究发现，尽管被指示从特定政治立场进行辩论，LLM智能体仍倾向于遵从模型固有的社会偏见。这种倾向导致其行为模式似乎偏离了人类之间已确立的社会动态。我们通过一种自动自微调方法强化了这些观察结果，该方法使我们能够操纵LLM内部的偏见，并证明智能体会随之与改变的偏见保持一致。这些结果强调了进一步研究开发帮助智能体克服这些偏见的方法的必要性，这是迈向创建更真实模拟的关键步骤。