This chapter discusses the scalability of context length in AI models, exploring the advantages of using language models with a larger number of tokens. It highlights the importance of longer windows for better inference and learning connections between distant elements. The chapter also speculates on the future possibility of adjusting the context window dynamically and proposes the idea of retention built into the transformer.

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