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
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50%
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Long-term
Relevance
7/19/2026
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Related Claims
VerifiedThe inconsistency of LLMs is closely related to the model's temperature parameter and stochastic sampling process, and multiple calls to the same model with the same question may produce different answers.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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