A greedy, ablation-based pruning method extracts a standalone task-specific subnetwork from GPT-2 Small, reducing parameters by up to 82.77% while keeping accuracy on three synthetic single-token tasks.
In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 8046–8056
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Extracting Interpretable Task-Specific Circuits from Large Language Models for Faster Inference
A greedy, ablation-based pruning method extracts a standalone task-specific subnetwork from GPT-2 Small, reducing parameters by up to 82.77% while keeping accuracy on three synthetic single-token tasks.