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$\rm SP^3$: Enhancing Structured Pruning via PCA Projection
- Publication Year :
- 2023
-
Abstract
- Structured pruning is a widely used technique for reducing the size of pre-trained language models (PLMs), but current methods often overlook the potential of compressing the hidden dimension (d) in PLMs, a dimension critical to model size and efficiency. This paper introduces a novel structured pruning approach, Structured Pruning with PCA Projection (SP3), targeting the effective reduction of d by projecting features into a space defined by principal components before masking. Extensive experiments on benchmarks (GLUE and SQuAD) show that SP3 can reduce d by 70%, compress 94% of the BERTbase model, maintain over 96% accuracy, and outperform other methods that compress d by 6% in accuracy at the same compression ratio. SP3 has also proven effective with other models, including OPT and Llama. Our data and code are available at an anonymous repo.<br />Comment: 21 pages
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2308.16475
- Document Type :
- Working Paper