First approximate calibration results for discrete properties in multiclass settings via Lipschitz intermediaries for strongly orderable discrete properties.
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Algorithms achieve adaptive calibration bounds of order min{sqrt(T) + (T C)^{1/3}, sqrt(K T)} for l1 error and min{(1+C)^{1/3}, K} for l2 and pseudo-KL error, where K and C are unknown non-stationarity measures.
Task calibration aligns LLM distributions in latent task spaces to make MBR decoding provably optimal and improve generation quality.
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Smoothed Elicitation Complexity for Approximate $\Gamma$-calibration of Discrete Classification Tasks
First approximate calibration results for discrete properties in multiclass settings via Lipschitz intermediaries for strongly orderable discrete properties.
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Adaptive Calibration in Non-Stationary Environments
Algorithms achieve adaptive calibration bounds of order min{sqrt(T) + (T C)^{1/3}, sqrt(K T)} for l1 error and min{(1+C)^{1/3}, K} for l2 and pseudo-KL error, where K and C are unknown non-stationarity measures.
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Task-Aware Calibration: Provably Optimal Decoding in LLMs
Task calibration aligns LLM distributions in latent task spaces to make MBR decoding provably optimal and improve generation quality.