<ul><li>Models exhibit a U-shaped attention pattern, focusing heavily on the beginning and end of the context and ignoring the middle, known as the 'donut hole' problem.</li><li>Increasing context window may lead to attention and accuracy drop in the middle 70-80% of the prompt, resulting in wastage of tokens.</li><li>Context inflation reshapes attention allocation, impacting the model's ability to focus, emphasizing the importance of task-specific prompt strategies.</li><li>Placing critical context at the beginning and end of prompts can significantly influence the model's output and improve performance in long contexts.</li></ul>

The Context Window Illusion: Why Your 128K Tokens Aren’t Working

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