LLMs' Fatal Flaw: The Lack of World Models
This essay delves into a fundamental flaw of Large Language Models (LLMs): their lack of robust cognitive models of the world. Using chess as a prime example, the author demonstrates how LLMs, despite memorizing game data and rules, fail to build and maintain dynamic models of the board state, leading to illegal moves and other errors. This isn't unique to chess; across various domains, from story comprehension and image generation to video understanding, LLMs' absence of world models results in hallucinations and inaccuracies. The author argues that building robust world models is crucial for AI safety, highlighting the limitations of current LLM designs in handling complex real-world scenarios and urging AI researchers to prioritize cognitive science in developing more reliable AI systems.