Unverified50% confidenceFactExact time
当前 LLM 在代码生成时是基于概率分布生成下一个 token，而非基于工程经验做出系统性设计决策
1
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50%
Confidence
Long-term
Relevance
8/23/2026
First Seen
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Related Claims
VerifiedThe generation mechanism of LLMs is probability-based text prediction, rather than truly 'understanding' code semantics80% similarUnverifiedAlthough LLMs do not directly output normalized probability vectors, decision confidence can be approximately estimated through various engineering methods.79% similarVerifiedLLMs generate output token by token in an autoregressive manner, with each word's selection based on probability sampling from the current context.79% similarVerifiedLLMs are essentially probability distribution estimators based on the Transformer architecture, and their output is a probability sampling over the next token.78% 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.78% similar
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