Unverified50% confidenceFactExact time
LLM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
1
Sources
50%
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% 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% 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.77% similarUnverifiedIn non-deterministic LLM systems, even when using the same prompt and parameters, outputs can differ due to the model's internal sampling mechanisms (temperature, top-p, etc.), API version updates, or floating-point arithmetic precision differences.77% similar
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