Logits-Based Block Pruning with Affine Transformations for Large Language Models (2026.findings-eacl)
Copied to clipboard
| 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. |
Similar Papers
Task-aware Block Pruning with Output Distribution Signals for Large Language Models (2026.findings-eacl)
Copied to clipboard
| Challenge: | Existing methods to estimate block importance rely on representation similarity or computationally expensive sensitivity analyses to estimate task-aware model behavior. |
| Approach: | They propose a novel approach that quantifies block-level uncertainty from the statistics of each block’s early-exited output distribution on a calibration dataset. |
| Outcome: | Experiments show that the proposed approach preserves downstream task performance while reducing inference latency and computational cost. |
FlexiGPT: Pruning and Extending Large Language Models with Low-Rank Weight Sharing (2025.naacl-long)
Copied to clipboard
James Seale Smith, Chi-Heng Lin, Shikhar Tuli, Haris Jeelani, Shangqian Gao, Yilin Shen, Hongxia Jin, Yen-Chang Hsu
| Challenge: | Empirical evaluations demonstrate substantial performance gains over existing methods . |
| Approach: | They propose a method to prune LLMs that selectively prunes model blocks based on an importance score and replaces them with a low-parameter replacement strategy. |
| Outcome: | The proposed method achieves state-of-the-art performance on 5/6 and 6/6 benchmarks with a compression rate of 30% and 40%. |
PDTrim: Targeted Pruning for Prefill-Decode Disaggregation in Inference (2026.acl-long)
Copied to clipboard
| Challenge: | Existing pruning methods ignore prefill-decode (PD) disaggregation in practice. |
| Approach: | They propose a pruning method that is highly integrated with prefill-decode (PD) disaggregation, enabling more precise pruning of blocks. |
| Outcome: | The proposed method achieves strong performance in both PD disaggregation and PD unified settings, and can be extended to other non-block pruning methods. |
BlockPruner: Fine-grained Pruning for Large Language Models (2025.findings-acl)
Copied to clipboard
| 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. |
GAP: a Global Adaptive Pruning Method for Large Language Models (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing structured pruning methods employ uniform compression rates across network layers, neglecting the varying importance of different network depths. |
| Approach: | They propose a pruning framework that minimizes global capability loss by layer-adaptive pruning rates. |
| Outcome: | The proposed approach achieves comparable performance with state-of-the-art methods at high pruning rates and shows significant advantages at low pruning rates. |
Rethinking Pruning Large Language Models: Benefits and Pitfalls of Reconstruction Error Minimization (2024.emnlp-main)
Copied to clipboard
| Challenge: | minimizing reconstruction error is not always ideal and can overfit calibration data. |
| Approach: | They propose a method to prune large language models by divide and conquer . they propose minimizing reconstruction error by more than 90% by using calibration data . |
| Outcome: | The proposed pruning approach generates high reconstruction errors . the proposed technique reduces reconstruction error by more than 90% . |
IG-Pruning: Input-Guided Block Pruning for Large Language Models (2025.emnlp-main)
Copied to clipboard
| 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 . |
DRPruning: Efficient Large Language Model Pruning through Distributionally Robust Optimization (2025.acl-long)
Copied to clipboard
| Challenge: | Structured pruning reduces model size but often causes uneven degradation across domains, leading to biased performance. |
| Approach: | They propose a method that dynamically adjusts the data distribution during training to restore balanced performance across heterogeneous and multi-tasking data. |
| Outcome: | Experiments in monolingual and multilingual settings show that the proposed method surpasses similarly sized models in pruning and continued pretraining over perplexity, downstream tasks, and instruction tuning. |
Towards Robust Pruning: An Adaptive Knowledge-Retention Pruning Strategy for Language Models (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing pruning strategies struggle to enhance robustness against adversarial attacks when continually increasing model sparsity and require a retraining process. |
| Approach: | They propose a pruning strategy that replicates embedding space and feature space of dense language models and aims to conserve more pre-trained knowledge during the pruning process. |
| Outcome: | The proposed pruning strategy replicates embedding space and feature space of dense language models, aiming to conserve more pre-trained knowledge during the pruning process. |
CFSP: An Efficient Structured Pruning Framework for LLMs with Coarse-to-Fine Activation Information (2025.coling-main)
Copied to clipboard
Yuxin Wang, MingHua Ma, Zekun Wang, Jingchang Chen, Shan Liping, Qing Yang, Dongliang Xu, Ming Liu, Bing Qin
| Challenge: | Existing LLM pruning works focus on unstructured pruning, which typically requires special hardware support for a practical speed-up. |
| Approach: | They propose a network pruning framework that leverages both coarse and fine-grained activation information as an importance criterion to guide pruning. |
| Outcome: | The proposed framework outperforms existing pruning methods on diverse models across sparsity budgets. |