Unverified95% confidenceFactTime unknown
LLM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
1
Sources
95%
Confidence
Long-term
Relevance
5/31/2026
First Seen
Sources
Agents Need Control Flow, Not More Prompts
bilibili锋芒AI
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Related Claims
UnverifiedLLM可通过分析输出token的对数概率分布、思维链末尾自我评估、温度采样统计答案分布熵值等手段近似估计决策置信度79% similarUnverifiedSummaries generated by LLMs are probabilistic; the same input may produce different results at different temperature settings, posing a reproducibility risk.78% 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% similarVerifiedLLM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78% similarVerifiedThe non-determinism of LLMs stems from sampling strategies; the Temperature parameter controls the randomness of the output distribution, while Top-p and Top-k further constrain the range of candidate tokens.77% similar
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