Summary of A Perspective on Large Language Models, Intelligent Machines, and Knowledge Acquisition, by Vladimir Cherkassky and Eng Hock Lee
A Perspective on Large Language Models, Intelligent Machines, and Knowledge Acquisition
by Vladimir Cherkassky, Eng Hock Lee
First submitted to arxiv on: 13 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates the limitations of Large Language Models (LLMs) in generating synthesized ‘knowledge’ that mimics human capabilities for understanding abstract concepts and reasoning. The study highlights the gap between LLMs and humans by analyzing GPT-4 responses to questions ranging from science and math to common sense reasoning, demonstrating that while LLMs can imitate human reasoning, they lack true understanding. The authors also discuss the impact of LLLs on the acquisition of human knowledge and education. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well Large Language Models (LLMs) can understand and reason like humans. They’re really good at making things look like they were written or said by a person, but it turns out that they don’t actually understand what they’re saying. The researchers found that even the most advanced LLMs, like GPT-4, can’t match human understanding because they rely on patterns in large amounts of data instead of having a deep understanding of abstract concepts. This has important implications for how we use these models to learn and teach. |
Keywords
* Artificial intelligence * Gpt
