Correlation-Aware Structured Pruning for Large Language Models
Structured pruning is a promising approach for reducing the substantial inference costs of Large Language Models (LLMs) while maintaining hardware efficiency. Many existing methods assess the importance of prunable units (e.g., channels or heads) in isolation, implicitly assuming that pruning errors are additive. This independence assumption is often invalidated by the non-orthogonality of model weights and strong correlations between unit activations, potentially leading to performance degradation. To address this, we propose a Correlation-Aware Structured Pruning method. We formulate the pruning objective as a cardinality-constrained binary quadratic program that explicitly models cross...
| Authors | Sicheng Xu, Hao Shi, Wei Zhang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 23 August 2026 |
| Last updated at source | 23 August 2026 |
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