Selective pruning of low-activation neurons in task-specific LLMs preserves accuracy better than random pruning, but removing roughly 10% of highly selective neurons triggers total collapse, with fine-tuning recovering much of the lost performance.
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Exploring the Limits of Pruning: Task-Specific Neurons, Model Collapse, and Recovery in Task-Specific Large Language Models
Selective pruning of low-activation neurons in task-specific LLMs preserves accuracy better than random pruning, but removing roughly 10% of highly selective neurons triggers total collapse, with fine-tuning recovering much of the lost performance.