Unverified50% confidenceFactExact time
Autoregressive models generate text token by token from left to right, and each step depends on the previously generated content.
1
Sources
50%
Confidence
Long-term
Relevance
10/16/2026
First Seen
Sources
Why Are Diffusion Language Models Generating So Much Buzz? Can Their Reasoning Truly Surpass Autoregressive LLMs?
redditr/deeplearning10/16/2026
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Related Claims
UnverifiedLarge language models generate text in an autoregressive manner, where the production of each token depends on the context formed by all preceding tokens, following the chain rule of probability.79% similarUnverifiedRanking models perform fine-grained sorting on top of initially recalled results, pushing the most relevant content to the front.72% similarUnverifiedWhen large models generate text, they do not generate an entire sentence at once; instead, they generate tokens one by one, continuously predicting the next token and adding it to the context in a loop.71% 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 prediction70% similarUnverifiedAutoregressive models work by taking the output of the previous step as the input for the next step, generating sequences incrementally.70% similar
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