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% similarVerifiedThe 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.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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