3 papers
cs.CL2026
EGAD: Entropy-Guided Adaptive Distillation for Token-Level Knowledge Transfer
Hao Zhang, Zhibin Zhang, Guangxin Wu +3
Large language models (LLMs) have achieved remarkable performance across diverse domains, yet their enormous computational and memory requirements hinder deployment in resource-con…
cs.CL2026
MI-PRUN: Optimize Large Language Model Pruning via Mutual Information
Hao Zhang, Zhibin Zhang, Guangxin Wu +3
Large Language Models (LLMs) have become indispensable across various domains, but this comes at the cost of substantial computational and memory resources. Model pruning addresses…
cs.CL2026
Iterative Structured Pruning for Large Language Models with Multi-Domain Calibration
Guangxin Wu, Hao Zhang, Zhang Zhibin +2
Large Language Models (LLMs) have achieved remarkable success across a wide spectrum of natural language processing tasks. However, their ever-growing scale introduces significant…