A Perspective on Large Language Models, Intelligent Machines, and Knowledge Acquisition
This is an incremental perspective on LLM limitations for AI researchers and educators, focusing on philosophical and practical impacts.
The paper analyzes the gap between large language models (LLMs) and human capabilities in understanding abstract concepts and reasoning, using GPT-4 examples to show it imitates but lacks true understanding, and discusses implications for knowledge acquisition and education.
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.