LLMs Aren't World Models: A Counterintuitive Argument
This article argues that Large Language Models (LLMs) don't truly understand the world, but excel at predicting text sequences. Through examples like chess, image blending modes, and Python multithreading, the author demonstrates that LLMs can generate seemingly reasonable answers while lacking understanding of underlying logic and rules. Even with corrections, LLMs struggle with basic concepts. The author posits that LLM success stems from engineering efforts, not genuine world understanding, and predicts breakthroughs in 'world models' leading to true general AI.
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