Verified65% confidenceFactExact time
Large 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.
3
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65%
Confidence
Long-term
Relevance
8/26/2026
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Related Claims
UnverifiedEach time a token is generated via a forward pass, the model must go through a complete multi-head attention computation and feed-forward network layers, with the amount of computation determined by the model's parameter scale78% similarUnverifiedDuring large language model inference, for each token generated, the model needs to re-read all parameters from memory.77% similarUnverifiedWhen 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.76% 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 do75% similarUnverifiedWord-by-word generation is technically called autoregressive generation, where the token generated at each step is appended back to the input sequence as the condition for the next prediction74% similar
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