Power transform, sign-preserving aggregation and 2% outlier removal let structured AFR match unstructured accuracy and beat prior structured pruners with ~1.57× speedup at 50% FFN sparsity.
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages=
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Structured Pruning of Large Language Models via Power Transformation and Sign-Preserving Score Aggregation with Adaptive Feature Retention
Power transform, sign-preserving aggregation and 2% outlier removal let structured AFR match unstructured accuracy and beat prior structured pruners with ~1.57× speedup at 50% FFN sparsity.