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In variational autoencoders, KL divergence is used to constrain the latent variable distribution to be close to the prior.
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11/3/2026
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UnverifiedIn token-level sequence generation tasks, the KL constraint is computed token by token, so tokens at different positions are subject to different constraint strengths.75% similarVerifiedKL divergence is used to compare the difference in output probability distributions between the fine-tuned model and the original model given the same input; a larger value indicates greater deviation, and a KLD of 0.0244 means the fine-tuning has hardly changed the model's overall behavioral distribution.74% similarUnverifiedIn subgroup discovery, KL divergence is used to quantify the degree to which the target variable distribution within a subgroup deviates from the global marginal distribution; the larger the KL divergence, the more anomalous the subgroup.74% similarUnverifiedKL divergence plays the role of a regularization term in variational autoencoders (VAEs), constraining the distributional shape of the latent space74% similarUnverifiedThe k-partition probing mechanism randomly divides committed token positions into k non-overlapping subsets, re-masking one subset at a time and performing one forward inference pass.72% similar
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