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Navigating Language Models and Human Comprehension
This chapter explores the relationship between large language models and human language processing, examining how these models predict and generate language structures. It highlights the limitations of LLMs in understanding meaning, using the Monty Hall problem as a case study to illustrate their misinterpretations. The discussion contrasts the cognitive mechanisms of humans with those of machines, emphasizing the nuances that language models often fail to capture.