Target-Aware Calibration Data Selection for Preserving Uncertainty in Quantized Language Models
Quantization is widely used to deploy large language models, but its effect on uncertainty behavior, such as confidence, margins, and abstention, is rarely treated as a primary objective. We frame calibration-data selection for quantization as a target-dependent uncertainty-preservation problem. Different deployments emphasize different regions of the input distribution, yet prior work mainly optimizes accuracy-oriented compression metrics or adjusts scores after quantization. We formalize this goal with distributional and boundary preservation risks, and provide a simple mixture-mismatch argu
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- PossiblePossibly related (embedding) · 55%[R] Statistically-Lossless Quantization of Large Language Models →
- PossiblePossibly related (embedding) · 54%[Paper] Statistically-Lossless Quantization of Large Language Models →
- PossiblePossibly related (embedding) · 52%Quantization →
- FuzzyOverlapping authors or contributors · 62%modular/modular →
“Shared author/contributor keys: liu”
- LinkedLinked via arxiv author · 85%Zhen Yang →
“Target-Aware Calibration Data Selection for Preserving Uncertainty in Quantized Language Models”
- LinkedLinked via arxiv author · 85%Sizai Hou →
“Target-Aware Calibration Data Selection for Preserving Uncertainty in Quantized Language Models”
- LinkedLinked via arxiv author · 85%Kaiwen Zheng →
“Target-Aware Calibration Data Selection for Preserving Uncertainty in Quantized Language Models”
- LinkedLinked via arxiv author · 85%Yaofang Liu →
“Target-Aware Calibration Data Selection for Preserving Uncertainty in Quantized Language Models”