  This paper delves into the dynamic landscape of artificial intelligence,
specifically focusing on the burgeoning prominence of large language models
(LLMs). We underscore the pivotal role of Reinforcement Learning from Human
Feedback (RLHF) in augmenting LLMs' rationality and decision-making prowess. By
meticulously examining the intricate relationship between human interaction and
LLM behavior, we explore questions surrounding rationality and performance
disparities between humans and LLMs, with particular attention to the Chat
Generative Pre-trained Transformer. Our research employs comprehensive
comparative analysis and delves into the inherent challenges of irrationality
in LLMs, offering valuable insights and actionable strategies for enhancing
their rationality. These findings hold significant implications for the
widespread adoption of LLMs across diverse domains and applications,
underscoring their potential to catalyze advancements in artificial
intelligence.
