Large Language Models (LLMs) are known for their remarkable ability to generate synthesized 'knowledge', such as text documents, music, images, etc. However, there is a huge gap between LLM's and human capabilities for understanding abstract concepts and reasoning. We discuss these issues in a larger philosophical context of human knowledge acquisition and the Turing test. In addition, we illustrate the limitations of LLMs by analyzing GPT-4 responses to questions ranging from science and math to common sense reasoning. These examples show that GPT-4 can often imitate human reasoning, even though it lacks understanding. However, LLM responses are synthesized from a large LLM model trained on all available data. In contrast, human understanding is based on a small number of abstract concepts. Based on this distinction, we discuss the impact of LLMs on acquisition of human knowledge and education.

本文探讨了大型语言模型（LLMs）在知识生成方面的能力与人类理解抽象概念的能力之间的差距。通过分析GPT-4在科学、数学和常识推理方面的回答，揭示了其在模仿人类推理的限制，指出人类理解基于少量抽象概念这一关键差异，进而讨论了LLMs对人类知识获取和教育的影响。

关于大型语言模型、智能机器与知识获取的观点