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 explicit probability distribution and sequence simulation with implicit 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.

“以大语言模型为代表的生成式人工智能迅猛发展,标志着人工智能从判别时代向生成时代的转变。这一进步极大地推动了信息检索技术的发展,本文对大语言模型对信息检索领域的影响进行了深入的综述。从性能改进到模式颠覆,逐步展开论述大语言模型对信息检索领域的影响。针对传统信息检索流程,大语言模型凭借强大的语义理解和建模能力,显著增强索引、检索和排序等信息检索模块的性能。同时,文章也探讨了大语言模型可能取代传统信息检索的趋势,并催生了新的信息获取方式,或将是新一次信息时代的寒武纪。此外,大语言模型对内容生态的深远影响也值得关注。”