Unverified60% confidenceFactExact time
LLMs generate text in an autoregressive manner, predicting the next token one at a time, with each prediction based on all prior context.
2
Sources
60%
Confidence
Long-term
Relevance
7/14/2026
First Seen
Sources
Prompt中一个词如何左右AI输出：可能性之井原理解析
Related Claims
VerifiedLLMs generate output token by token in an autoregressive manner, with each word's selection based on probability sampling from the current context.84% similarUnverifiedLLMs generate code by predicting 'the most likely next token to appear in the current context.'81% 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.78% similarUnverifiedA 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 loop78% similarVerifiedLLMs generate text in an autoregressive manner based on the Transformer architecture, predicting only one token per forward pass and then appending that token to the input sequence before predicting the next token.77% similar
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