Unverified50% confidenceFactExact time
LLM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
1
Sources
50%
Confidence
Long-term
Relevance
7/7/2026
First Seen
Sources
Regression to the Mean: How LLMs Are Quietly Killing Innovation and Diversity
hackernewshackernews7/6/2026
Related Claims
VerifiedWhen 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.82% similarUnverifiedTransformer模型通过注意力机制（Self-Attention）在推理阶段在词汇表上生成一个概率分布，再通过温度参数（Temperature）调节分布的平坦度，温度越高输出越随机多样，温度越低越趋向高概率词元80% similarUnverifiedLLM本质上是在做下一个token的概率预测，即便温度参数设为0，面对复杂的多步骤指令时，模型仍会受到注意力机制的影响77% similarUnverifiedLarge language models are based on the Transformer architecture, and their output is essentially a probabilistic sampling process, with the Temperature parameter controlling the randomness of sampling.77% similarVerifiedLLMs are essentially probability distribution estimators based on the Transformer architecture, and their output is a probability sampling over the next token.76% similar
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