Unverified50% confidenceFactExact time
大语言模型（LLM）由于temperature、top-p、top-k等采样策略带有随机性，同一提示词在两次调用中可能产生截然不同的响应
1
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50%
Confidence
Long-term
Relevance
7/19/2026
First Seen
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Related Claims
VerifiedLLM的不一致性与模型的温度参数和随机采样过程密切相关，同一问题多次调用同一模型可能产生不同答案83% similarUnverified在LLM引用行为测试中固定随机性需要同时设置temperature和top-p参数并在支持时固定随机种子81% 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.80% 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.79% 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.78% similar
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