With the rapid advancement of large language models (LLMs) for handling complex language tasks, an increasing number of studies are employing LLMs as agents to emulate the sequential decision-making processes of humans often represented as Markov decision-making processes (MDPs). The actions in MDPs adhere to specific probability distributions and require iterative sampling. This arouses curiosity regarding the capacity of LLM agents to comprehend probability distributions, thereby guiding the agent's behavioral decision-making through probabilistic sampling and generating behavioral sequences. To answer the above question, we divide the problem into two main aspects: sequence simulation with known probability distribution and sequence simulation with unknown probability distribution. Our analysis indicates that LLM agents can understand probabilities, but they struggle with probability sampling. Their ability to perform probabilistic sampling can be improved to some extent by integrating coding tools, but this level of sampling precision still makes it difficult to simulate human behavior as agents.

翻译：随着大语言模型（LLMs）在处理复杂语言任务方面的快速发展，越来越多的研究将LLMs作为智能体来模拟人类通常被建模为马尔可夫决策过程（MDPs）的序列决策过程。MDPs中的行动遵循特定的概率分布并需要迭代采样。这引发了关于LLM智能体理解概率分布能力的好奇心，从而通过概率采样指导智能体的行为决策并生成行为序列。为回答上述问题，我们将问题分解为两个主要方面：已知概率分布的序列模拟和未知概率分布的序列模拟。我们的分析表明，LLM智能体能够理解概率，但在概率采样方面存在困难。通过集成编码工具，其执行概率采样的能力可以在一定程度上得到提升，但此级别的采样精度仍难以作为智能体模拟人类行为。