Unverified50% confidenceFactExact time
大语言模型输出的非确定性源于 Transformer 解码阶段的随机采样机制，采样温度、Top-P 等参数决定每次生成内容可能不同
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50%
Confidence
Long-term
Relevance
7/17/2026
First Seen
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Related Claims
UnverifiedLarge 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.82% similarUnverifiedThe output of large language models is inherently probabilistic, and parameters such as sampling temperature and Top-P mean the model may generate different content each time, posing a challenge for software testing.77% 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76% similarUnverifiedThe temperature parameter design in the language model sampling process means that the same prompt may produce completely different outputs across different calls, so retrying itself does not guarantee convergence.76% similarVerifiedWhen a large language model generates each token, it samples from a probability distribution; the temperature parameter controls the degree of randomness—higher temperature produces more diverse outputs, while lower temperature produces more deterministic ones.76% similar
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