Expired50% confidenceFactExact time
Model generation is an autoregressive serial process that generates only one token at a time, causing the output phase to be insufficiently parallelizable and computationally more intensive than the input encoding phase.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
7/2/2026
First Seen
Valid until: 9/30/2026(expired)
Sources
AI聚合平台免费用顶级模型？冷静分析与避坑指南
bilibili子非鱼EE6/4/2026
Related Claims
UnverifiedAutoregressive Generation is a process where the model generates one token at a time, appending it to the existing sequence as input for the next prediction76% similarUnverifiedThe inference speed of large language models is constrained by the nature of autoregressive generation, which can only produce one token at a time and depends on all preceding tokens, making true parallelization impossible.71% similarUnverifiedLarge language models are autoregressive generation models where each output step depends on the preceding context.71% similarUnverifiedLanguage models work through autoregressive generation: given all preceding tokens, they predict the probability distribution of the next token and sample an output.69% similarVerifiedThe token generation process of large language models is serial, based on an autoregressive mechanism, where each token's generation depends on the previous token.69% similar
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