A Perspective on Large Language Models, Intelligent Machines, and Knowledge Acquisition

• URL: http://arxiv.org/abs/2408.06598v1
• Date: Tue, 13 Aug 2024 03:25:49 GMT
• Title: A Perspective on Large Language Models, Intelligent Machines, and Knowledge Acquisition
• Authors: Vladimir Cherkassky, Eng Hock Lee,
• Abstract summary: Large Language Models (LLMs) are known for their remarkable ability to generate 'knowledge' 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.
• Score: 0.6138671548064355
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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