Unverified50% confidenceFactExact time
Entropy minimization drives predictions toward one-hot by minimizing the Shannon entropy of the prediction distribution, but it does not test whether the predictions are correct.
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11/3/2026
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Related Claims
UnverifiedFinite-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.80% similarUnverifiedJudgments based on a model's own output confidence (token probability/entropy) add almost no extra VRAM overhead.78% similarPartially VerifiedThe more certain a distribution is, the lower its entropy and the shorter the required encoding; the more random a distribution is (such as a uniform distribution), the higher its entropy.74% similarUnverifiedMinimizing cross-entropy is equivalent to minimizing the KL divergence between the true distribution and the predicted distribution.74% similarUnverifiedAssuming the data follows a Bernoulli or multinomial distribution, maximizing the likelihood is equivalent to minimizing the cross-entropy loss function.72% similar
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