Unverified50% confidenceFactExact time
Word-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 prediction
1
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50%
Confidence
Long-term
Relevance
7/22/2026
First Seen
Sources
Related Claims
UnverifiedAutoregressive models work by taking the output of the previous step as the input for the next step, generating sequences incrementally.76% similarUnverifiedAutoregressive generation refers to the model predicting the next token each time based on all previously generated tokens, progressively unfolding until a complete code block is generated75% similarUnverifiedWhen a prompt is entered, the model predicts the most likely next token based on context, rather than truly understanding the semantics.75% similarUnverifiedAutoregressive generative models sample the next token at each step based on the probability distribution of the preceding context; when a repetitive pattern gains a slightly higher probability, it forms a positive feedback loop that leads to repetition degeneration.74% similarUnverifiedModern large language models generate code in an auto-regressive manner, predicting the next token each time by referencing only the existing context.74% similar
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