Author

Zhen Yang

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Preprint Aug 2026

Calibrating Post-Training Feature Shifts for LLM Data Contamination Detection

CalibDCD is proposed, a broadly applicable calibration framework for feature-based DCD methods, comprising Multi-View Shift Detection, which identifies recurring feature shifts associated with post-training, and Bounded Feature Correction, which selectively mitigates their influence on membership prediction.

Zhen Yang, Mengqi Wang, Gengda Zhao et al. · 0 citations

Preprint Aug 2026

Target-Aware Calibration Data Selection for Preserving Uncertainty in Quantized Language Models

DPQ is introduced, a lightweight pre-quantization recipe family that uses full-precision predictions to construct target-aligned calibration mixtures of high-doubt examples and generic anchors that better preserve broad multiple-choice QA behavior.

Zhen Yang, Sizai Hou, Kai-Wen Zheng et al. · 1 citation