大型语言模型与人类理性之比较：洞见与未解问题
Comparing Rationality Between Large Language Models and Humans: Insights and Open Questions
AI总结：
本文通过比较分析，探讨了大型语言模型与人类在理性上的差异，强调了RLHF在提升模型理性中的作用，并提出了增强LLM理性的策略，对AI发展具有重要启示。
摘要：
本文深入探讨了人工智能的动态格局，特别聚焦于大型语言模型（LLMs）日益凸显的重要性。我们强调了基于人类反馈的强化学习（RLHF）在增强LLMs的理性和决策能力方面的关键作用。通过细致审视人类交互与LLM行为之间的复杂关系，我们探讨了关于人类与LLM之间理性及性能差异的问题，尤其关注Chat生成预训练变换器。我们的研究采用了全面的比较分析，并深入探讨了LLMs中固有的非理性挑战，为提升其理性提供了宝贵的洞见和可行的策略。这些发现对LLMs在多个领域和应用中的广泛采用具有重要意义，凸显了其推动人工智能进步的潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
