908 citations · 2.4k across the 149 of their papers we have counts for
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cs.CL2022★ 1 cited
GMP*: Well-Tuned Gradual Magnitude Pruning Can Outperform Most BERT-Pruning Methods
Eldar Kurtic, Dan Alistarh
We revisit the performance of the classic gradual magnitude pruning (GMP) baseline for large language models, focusing on the classic BERT benchmark on various popular tasks. Despi…
cs.CL2022★ 2 cited
The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models
Eldar Kurtic, Daniel Campos, Tuan Nguyen +5
Transformer-based language models have become a key building block for natural language processing. While these models are extremely accurate, they can be too large and computation…