<ul><li>Researchers propose a novel pruning method called structurally-aware adaptive pruning (SAAP) for large language models (LLMs).</li><li>SAAP aims to reduce computational and memory costs while maintaining model performance on resource-constrained edge devices.</li><li>The method defines an adaptive importance fusion metric to evaluate the importance of all coupled structures in LLMs.</li><li>Experimental results show that SAAP outperforms several state-of-the-art baseline methods in terms of accuracy and token generation speed.</li></ul>

Adaptive Pruning for Large Language Models with Structural Importance Awareness

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