GPrune-LLM: Generalization-Aware Structured Pruning for Large Language Models
Structured pruning is widely applied to compress large language models (LLMs), but its performance depends heavily on how neuron importance is estimated. Most existing methods rely on activation statistics from a single calibration set, which introduces calibration bias and degrades downstream cross-task generalization. We observe that neurons exhibit heterogeneous distribution sensitivity, ranging from maintaining relatively stable rankings across calibration datasets to showing substantially larger cross-dataset variation. Ignoring this heterogeneity, existing methods rank all neurons in shared spaces with a uniform scoring source, so calibration-specific neurons dominate the ranking an...
| Authors | Xiaoyun Liu, Divya Saxena, Jiannong Cao et al. |
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| Primary category | cs.LG |
| Published | 12 March 2026 |
| Last updated at source | 3 August 2026 |
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