This article explores distinguishing details of Markov Models and Transformer-based models like GPT, focusing on how they predict the next character in a sequence. It explores the fundamental differences between these models, with a particular emphasis on how the self-attention mechanism in Transformer models makes a difference compared to the fixed context length in Markov models.

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