Verified65% confidenceFactExact time
LLMs generate output token by token in an autoregressive manner, with each word's selection based on probability sampling from the current context.
3
Sources
65%
Confidence
Long-term
Relevance
7/21/2026
First Seen
Sources
Related Claims
UnverifiedLLMs generate text in an autoregressive manner, predicting the next token one at a time, with each prediction based on all prior context.84% similarPartially VerifiedLLMs use an autoregressive generation mechanism, sampling the next token at each step based solely on a probability distribution.83% similarUnverifiedLLMs are essentially next-token prediction machines based on statistical patterns, and their output is the text sequence with the highest probability given the context.82% similarUnverifiedLLM生成的每一个词本质上是基于前文语境的条件概率预测，而非基于真实经历或情感状态82% similarVerifiedLLMs are fundamentally probability distribution systems trained on massive amounts of text, where each output is a sampling of the next most likely token rather than retrieval and reasoning based on real-world knowledge.82% similar
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