Expired50% confidenceFactExact time
Autoregressive Generation is a process where the model generates one token at a time, appending it to the existing sequence as input for the next prediction
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
7/2/2026
First Seen
Valid until: 9/30/2026(expired)
Sources
Core Methodology of Prompt Engineering: A Systematic Deep Dive from Principles to Practice
bilibili大模型研究所5/27/2026
Related Claims
UnverifiedModel 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.76% similarUnverifiedLanguage models work through autoregressive generation: given all preceding tokens, they predict the probability distribution of the next token and sample an output.72% 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 generated71% similarUnverifiedLLMs use an autoregressive generation mechanism — predicting the next most likely token based on existing context, one at a time69% similarUnverified元胞自动机是一种离散的计算模型，由规则网格构成，每个元胞处于有限状态之一，系统按照固定的局部规则同步更新每个元胞的状态68% similar
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