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Tag

#calibration

5 articles tagged #calibration

arxivJun 3bullish

Before Fusion, Ask What to Keep: Contextual Calibration of Multimodal Signals

arXiv:2606.02679v1 Announce Type: new Abstract: Multimodal systems often benefit from combining information across language, sound, and visual streams, but this benefit is not guaranteed. A modality that is useful for one input may become distracting for another, and local feature responses within t

#multimodal#fusion#calibrationRead on arxiv →
arxivMay 29

Calibrating Generative Models to Distributional Constraints

arXiv:2510.10020v4 Announce Type: replace-cross Abstract: Generative models frequently suffer miscalibration, wherein statistics of the sampling distribution, such as the fraction of generations in a given class, deviate from desired values. We frame calibration as a constrained optimization problem

#machine-learning#calibration#optimizationRead on arxiv →
arxivMay 22

Expectation Consistency Loss: Rethink Confidence Calibration under Covariate Shift

arXiv:2605.21552v1 Announce Type: new Abstract: Confidence calibration for classification models is vital in safety-critical decision-making scenarios and has received extensive attention. General confidence calibration methods assume training and test data are independent and identically distribute

#calibration#covariate-shift#domain-adaptationRead on arxiv →
arxivMay 4

Prompt-Induced Score Variance in Zero-Shot Binary Vision-Language Safety Classification

arXiv:2605.00326v1 Announce Type: new Abstract: Single-prompt first-token probabilities from zero-shot vision-language model (VLM) safety classifiers are treated as decision scores, but we show they are unreliable under semantically equivalent prompt reformulation: even when the binary label is cons

ZE1 model#safety#benchmark#calibrationRead on arxiv →
arxivMay 1

Geometry-Calibrated Conformal Abstention for Language Models

arXiv:2604.27914v1 Announce Type: new Abstract: When language models lack relevant knowledge for a given query, they frequently generate plausible responses that can be hallucinations, rather than admitting being agnostic about the answer. Retraining models to reward admitting ignorance can lead to

#conformal-prediction#language-models#calibrationRead on arxiv →
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