Partially Verified65% confidenceFactExact time
LLMs use an autoregressive generation mechanism, sampling the next token at each step based solely on a probability distribution.
3
Sources
65%
Confidence
Long-term
Relevance
7/16/2026
First Seen
Sources
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.83% similarVerifiedWhen generating each token, LLMs randomly sample from a probability distribution according to a temperature parameter rather than selecting the deterministic output with the highest probability, thereby producing emergent properties.81% similarVerifiedThe root cause of LLM hallucination lies in the autoregressive generation mechanism—the model predicts the next Token based on a probability distribution rather than genuine logical reasoning.79% similarUnverifiedLarge language models use an autoregressive generation mechanism, sampling the next token at each step based solely on a probability distribution, with each token's generation conditioned on all preceding tokens to form a chain of probabilistic dependencies.79% similarUnverifiedLLM虽然不直接输出归一化概率向量，但可通过多种工程手段近似估计决策置信度78% similar
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