Unverified50% confidenceFactExact time
A typical LLM uses an autoregressive generation approach, predicting token by token with each step's output feeding into the next step as input, forming a loop
1
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50%
Confidence
Long-term
Relevance
9/27/2026
First Seen
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Related Claims
UnverifiedLLMs generate text in an autoregressive manner, producing only one token at a time, requiring N forward inference passes to generate N tokens, with latency scaling linearly with output length.79% similarUnverifiedLLMs use an autoregressive generation mechanism — predicting the next most likely token based on existing context, one at a time78% similarUnverifiedLLMs generate text in an autoregressive manner, predicting the next token one at a time, with each prediction based on all prior context.78% similarUnverifiedLLMs are essentially next-token prediction machines based on statistical patterns, and their output is the text sequence with the highest probability given the context.76% similarVerifiedLLMs generate output token by token in an autoregressive manner, with each word's selection based on probability sampling from the current context.76% similar
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