Unverified50% confidenceFactExact time
Softmax函数将神经网络logits压缩为概率分布，但其输出值在大规模模型中系统性偏离真实贝叶斯后验概率
1
Sources
50%
Confidence
Long-term
Relevance
7/8/2026
First Seen
Sources
元认知反馈：用强化学习让大模型准确表达不确定性
hackernewshackernews7/7/2026
Related Claims
UnverifiedSoftmax输出的概率值并不等同于真实的贝叶斯后验概率，而是被过度优化以拉开类别间距，导致预测概率系统性偏高81% similarUnverifiedSoftmax函数具有平移不变性，任意放大所有logit的尺度不改变预测类别，却会将输出概率压向极端值，是其天然的过度自信倾向80% similarUnverifiedWhen a model encounters out-of-distribution inputs, prediction entropy increases, the softmax output tends toward a uniform distribution, and the range of candidate tokens permitted by nucleus sampling expands, giving long-tail anomalous tokens a chance of being sampled.72% similarUnverified计算 softmax 函数时指数运算可能导致浮点数溢出，标准做法是先减去输入向量的最大值再取指数69% similarUnverified结构化提示（角色设定+任务背景+约束条件+输出格式）能降低模型的解码不确定性，使 softmax 概率质量集中到预期输出区间69% similar
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