Papers with Model compression
AlphaTuning: Quantization-Aware Parameter-Efficient Adaptation of Large-Scale Pre-Trained Language Models (2022.findings-emnlp)
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Se Jung Kwon, Jeonghoon Kim, Jeongin Bae, Kang Min Yoo, Jin-Hwa Kim, Baeseong Park, Byeongwook Kim, Jung-Woo Ha, Nako Sung, Dongsoo Lee
| Challenge: | Existing approaches to improve inference efficiency by accelerating model fine-tuning have not been thoroughly explored. |
| Approach: | They propose to combine parameter-efficient adaptation and model compression to accelerate model . they propose to freeze binary parameters and scale scaling factors for target tasks . |
| Outcome: | The proposed algorithm achieves >10x compression ratio under 4-bit quantization and >1,000x reduction in trainable parameters. |
Train Flat, Then Compress: Sharpness-Aware Minimization Learns More Compressible Models (2022.findings-emnlp)
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| Challenge: | Recent advances in hardware, modeling, and optimization for deep neural networks have led to improvements in memory and inference efficiency. |
| Approach: | They propose to combine sharpness-aware minimization with various model compression methods to improve model compressibility. |
| Outcome: | Empirically, optimizing for flatter minima leads to greater compressibility of parameters compared to vanilla Adam when fine-tuning BERT models, with little to no loss in accuracy on the GLUE text classification and SQuAD question answering benchmarks. |
Less Is More? Examining Fairness in Pruned Large Language Models for Summarising Opinions (2025.emnlp-main)
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| Challenge: | reducing the size of LLMs through post-training pruning has been studied, but its impact on model fairness remains unexplored. |
| Approach: | They propose a pruning method that removes parameters that are redundant for input processing but influential in output generation. |
| Outcome: | The proposed pruning method can maintain or improve fairness across models and tasks where existing methods have limitations. |