Verified75% confidenceFactExact time
LLMs are essentially probability distribution estimators based on the Transformer architecture, and their output is a probability sampling over the next token.
5
Sources
75%
Confidence
Long-term
Relevance
7/8/2026
First Seen
Sources
并非一切都值得消耗Token：何时用AI，何时用确定性代码
hackernewshackernews7/6/2026
Related Claims
Unverified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.76% similarUnverifiedLLM基于Transformer架构的自注意力机制和Softmax概率分布，通过温度参数控制采样随机性，温度趋近0时输出最高概率token，温度升高时引入更多随机性76% similarVerifiedLLMs generate output token by token in an autoregressive manner, with each word's selection based on probability sampling from the current context.76% similarVerifiedThe autoregressive generation characteristic of LLMs determines that their output is streamed token by token, stemming from the decoding strategy of the Transformer architecture.75% similar
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