Comparing Rationality Between Large Language Models and Humans: Insights and Open Questions

TL;DR

This paper compares the rationality of large language models and humans, analyzing how reinforcement learning from human feedback influences LLM decision-making and highlighting challenges and strategies for improving their rationality.

Contribution

It provides a comprehensive comparison of human and LLM rationality, emphasizing the role of RLHF and exploring open questions and challenges in LLM decision-making.

Findings

RLHF enhances LLM rationality and decision-making.

Disparities exist between human and LLM rationality and performance.

Strategies for improving LLM rationality are discussed.

Abstract

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.