Unverified50% confidenceFactExact time
Uncertainty scores are often based on predictive entropy, confidence or Bayesian approximation, and high-uncertainty samples often lie near the decision boundary.
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10/15/2026
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UnverifiedExisting uncertainty estimation schemes usually require multiple samplings (5x or more compute), the introduction of a judge model, or reliance on labeled datasets, making them costly and only giving general confidence.75% similarUnverified不确定性估计主要有两条技术路径：利用Token概率（logprobs）和蒙特卡洛采样（对同一输入进行5-20次高温度采样并计算香农熵）75% similarUnverifiedThe inherent uncertainty of the data distribution is measured by source entropy, which can be estimated with a finite-sample confidence lower bound using samples.75% similarUnverifiedThe credible interval in Bayesian methods can be directly interpreted as the probability that a parameter falls within the range, providing a more intuitive quantification of uncertainty than the frequentist confidence interval.73% similarUnverifiedInterval forecasting provides an uncertainty range for the predicted value, and is more practical than point forecasting for highly volatile B2B businesses.71% similar
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