Unverified50% confidenceSolutionExact time
Judgments based on a model's own output confidence (token probability/entropy) add almost no extra VRAM overhead.
1
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50%
Confidence
Long-term
Relevance
9/12/2026
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Unverified当模型对某个训练样本的预测概率接近1时,该样本对应的交叉熵损失趋近于零,模型几乎无法获得梯度更新信号69% similarUnverifiedFinite-sample estimation of entropy rate faces a systematic negative bias problem, where plug-in estimators tend to underestimate the true entropy; this can be compensated using the Miller-Madow correction by adding a (k-1)/(2N) term.69% similarUnverifiedFor small models with limited parameter counts, data quality can have an even greater impact on final performance than data quantity.68% similarUnverified在生产环境中,一个平均水平稍低但表现稳定的模型的实际价值往往高于平均水平更高但输出质量波动剧烈的模型68% similarUnverifiedLarge model outputs are probabilistic and non-deterministic; running the same prompt on the same version of a model multiple times may produce different results, making quality judgments in gradual rollouts difficult.68% similar
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