Unverified70% confidenceFactExact time
传统推测解码的验证过程对草稿提出的token以min(1, p(x)/q(x))的概率接受,被拒绝则从修正分布norm(max(0, p(x)-q(x)))重新采样,可证明产生的token分布与直接从目标模型采样完全一致(lossless)
2
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70%
Confidence
Long-term
Relevance
9/7/2026
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Unverified在机器学习多种子实验中重复次数通常只有5到10次,样本量极小,正态性假设难以验证,因此 Wilcoxon 检验往往是更保守稳健的选择68% similarUnverified当z>4时,在零假设下观察到如此极端值的概率约为p≈3.2×10⁻⁵,即每十万次检测中约3次误报67% similarUnverifiedLLM每次调用可建模为伯努利试验,N次调用通过次数服从二项分布B(N, p),通过率95%置信区间约为p̂ ± 1.96×√(p̂(1-p̂)/N)67% similarUnverified马尔可夫头仅需记录前一个token状态即可推导下一token修正概率,参数量通常不超过原模型的0.1%67% similarUnverified在某些测试条件下,主流AI编程工具推荐不存在包名的概率可达5%~20%67% similar
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