Pruning Redundant Mappings in Transformer Models via Spectral-Normalized Identity Prior (2020.findings-emnlp)
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| Challenge: | Spectral-normalized identity priors (SNIP) is a structured pruning approach for a Transformer model. |
| Approach: | They propose a structured pruning approach which penalizes an entire residual module toward an identity mapping. |
| Outcome: | The proposed method improves on 5 GLUE benchmark tasks while maintaining comparable performance. |
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| Challenge: | Recent work shows that a large proportion of the heads in a Transformer’s multi-head attention mechanism can be safely pruned away without significantly harming the performance of the model. |
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| Challenge: | Large-scale generative Pre-trained Language Models (PLMs) are limited in their deployment in real-world applications. |
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On the Distribution, Sparsity, and Inference-time Quantization of Attention Values in Transformers (2021.findings-acl)
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Tianchu Ji, Shraddhan Jain, Michael Ferdman, Peter Milder, H. Andrew Schwartz, Niranjan Balasubramanian
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| Challenge: | Existing pruning results on benchmark transformers, such as BERT, are not as remarkable as those of convolutional neural networks. |
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Focus on the Core: Efficient Attention via Pruned Token Compression for Document Classification (2023.findings-emnlp)
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| Challenge: | Pre-trained transformers suffer from a computationally expensive self-attention mechanism that interacts with all tokens, including those unfavorable to classification performance. |
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Two-Stage Regularization-Based Structured Pruning for LLMs (2026.acl-long)
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Mingkuan Feng, Jinyang Wu, Siyuan Liu, Shuai Zhang, Hongjian Fang, Ruihan Jin, Feihu Che, Pengpeng Shao, Zhengqi Wen, Jianhua Tao
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| Challenge: | Structured pruning is an effective technique for compressing pre-trained language models (PLMs), but it requires retraining, leading to additional computational overhead. |
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SP3: Enhancing Structured Pruning via PCA Projection (2024.findings-acl)
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| Challenge: | Structured pruning is a widely used technique for reducing the size of pre-trained language models, but current methods overlook the potential of compressing the hidden dimension d in PLMs. |
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Block Pruning For Faster Transformers (2021.emnlp-main)
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| Challenge: | Pruning methods have proven to be effective at reducing model size, while distillation methods are proven for speeding up inference. |
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