Unverified50% confidenceFactExact time
When a large language model processes input, it computes Key and Value vectors in the attention mechanism for each token, and these vectors are reused repeatedly when generating replies.
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Long-term
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10/20/2026
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VerifiedWhen large language models process long contexts, the computational complexity of the attention mechanism grows proportionally to the square of the sequence length.78% similarVerifiedThe self-attention mechanism allows the model to simultaneously reference the contextual information of all other tokens in a sequence when processing each token, rather than processing sequentially as RNNs do78% similarUnverifiedThe context window refers to the total number of tokens a model can see in a single inference, including input and output; the computational cost of the attention mechanism grows quadratically with sequence length.77% similarUnverifiedIn large language model inference, KV-Cache is used to store the key-value pairs already computed in the attention mechanism, thereby avoiding repeated computation.77% similarVerifiedLarge model output generation uses an autoregressive approach to produce tokens one by one, requiring recalculation of attention over all preceding context with each token generated.76% similar
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