Verified75% confidenceFactExact time
The 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.
5
Sources
75%
Confidence
Long-term
Relevance
7/15/2026
First Seen
Sources
Related Claims
Unverified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83% similarVerifiedWhen 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.78% similarUnverifiedThe randomness of LLM inference comes from at least three independent sources: the sampling strategy (temperature, top-p), the accumulated error of floating-point operations, and changes in the reduction order of matrix multiplications caused by changes in batch size.78% 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78% similarVerifiedThe 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.77% similar
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