| Challenge: | Pruning methods have proven to be effective at reducing model size, while distillation methods are proven for speeding up inference. |
| Approach: | They propose a block pruning approach that integrates structured pruning methods with the movement pruning paradigm for fine-tuning. |
| Outcome: | The proposed model is 2.4x faster, 74% smaller and faster than distilled models on classification and generation tasks. |
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Structured Pruning Learns Compact and Accurate Models (2022.acl-long)
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| Challenge: | Pre-trained language models have high costs in terms of storage, memory, and computation time. |
| Approach: | They propose a task-specific structured pruning method CoFi which provides highly parallelizable subnetworks and matches distillation methods in both accuracy and latency. |
| Outcome: | The proposed method matches the distillation methods in accuracy and latency without resorting to unlabeled data. |
BlockPruner: Fine-grained Pruning for Large Language Models (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have significant computational and memory costs associated with training and inference. |
| Approach: | They propose a training-free structured pruning approach that targets redundancies in MHA and MLP blocks. |
| Outcome: | The proposed pruning approach achieves more granular and effective pruning compared to state-of-the-art pruning methods. |
Rethinking Network Pruning – under the Pre-train and Fine-tune Paradigm (2021.naacl-main)
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| Challenge: | Existing pruning results on benchmark transformers, such as BERT, are not as remarkable as those of convolutional neural networks. |
| Approach: | They propose to apply a knowledge-aware pruning process to transformer-based pre-trained language models to reduce model size and model weight. |
| Outcome: | The proposed pruning method outperforms the leading competitors with a 20-times weight/FLOPs compression and neglectable loss in prediction accuracy. |
Differentiable Subset Pruning of Transformer Heads (2021.tacl-1)
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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. |
| Approach: | They propose a method that prunes a Transformer's multi-head attention mechanism away without significantly harming its performance. |
| Outcome: | The proposed method improves on natural language inference and machine translation tasks while offering precise control of sparsity level. |
Structured Pruning for Efficient Generative Pre-trained Language Models (2023.findings-acl)
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| Challenge: | Large-scale generative Pre-trained Language Models (PLMs) are limited in their deployment in real-world applications. |
| Approach: | They propose to prune the feed-forward networks of generative pre-trained language models to smaller widths without designing extra operators. |
| Outcome: | The proposed method achieves 1.51x/6.96x inference speedup on GPU/CPU with 67% size reduction. |
Pruning before Fine-tuning: A Retraining-free Compression Framework for Pre-trained Language Models (2024.lrec-main)
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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. |
| Approach: | They propose a task-specific pruning framework that prunes redundant modules of pre-trained language models before fine-tuning them. |
| Outcome: | The proposed pruning framework achieves higher performance on GLUE, SQUAD, WikiText-2, Wik-103, and PTB datasets while reducing the time required for fine-tuning. |
Structured Pruning of Large Language Models (2020.emnlp-main)
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| Challenge: | Recent advances in language modeling have led to remarkable improvements on a variety of tasks. |
| Approach: | They propose a generic, structured pruning approach by parameterizing each weight matrix and adaptively removing rank-1 components during training. |
| Outcome: | The proposed method outperforms unstructured pruning and block pruning on language modeling tasks while achieving speedups during training and inference. |
Parameter-Efficient Transfer Learning with Diff Pruning (2021.acl-long)
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| Challenge: | Pretrained networks are difficult to deploy for multiple tasks in storage-constrained settings. |
| Approach: | Diff pruning enables parameter-efficient transfer learning that scales well with new tasks. |
| Outcome: | Diff pruning can match the performance of finetuned baselines on the GLUE benchmark while only modifying 0.5% of the pretrained model’s parameters per task. |
IG-Pruning: Input-Guided Block Pruning for Large Language Models (2025.emnlp-main)
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| Challenge: | Existing methods for efficient inference rely on fixed block masks, which can lead to suboptimal performance. |
| Approach: | They propose an input-aware block-wise pruning method that dynamically selects layer masks at inference time. |
| Outcome: | The proposed method outperforms state-of-the-art static depth pruning methods . it is particularly suitable for resource-constrained deployment scenarios . |
Logits-Based Block Pruning with Affine Transformations for Large Language Models (2026.findings-eacl)
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| Challenge: | Existing methods for pruning models rely on calibration data and neglect cumulative effects of pruning on subsequent blocks. |
| Approach: | They propose to use the Logit Disruption Score (LDS) to measure the impact of pruning by comparing the cosine similarity between the logits of the original and pruned models. |
| Outcome: | Experiments on multiple datasets show that the proposed pruning technique reduces reliance on calibration data and improves generalization, achieving competitive results with existing methods. |