| Challenge: | Existing studies have focused on mitigating memorization, but the deliberate control of memorisation has been underexplored. |
| Approach: | They propose a gradient-based weight pruning framework to control memorization rates in large language models by fine-grained control over pruning parameters. |
| Outcome: | The proposed framework enables models to suppress or enhance memorization based on application-specific requirements. |
Similar Papers
Mitigating Catastrophic Forgetting in Large Language Models with Forgetting-aware Pruning (2025.emnlp-main)
Copied to clipboard
| Challenge: | Recent advances in large language models (LLMs) have shown impressive capabilities in various downstream tasks but typically face Catastrophic Forgetting (CF) during fine-tuning. |
| Approach: | They propose a pruning-based approach to balance CF and downstream task performance by integrating the ratio of the task vector to pre-trained model parameters into the pruning criteria. |
| Outcome: | The proposed pruning-based approach limits CF to just 0.25% while maintaining 99.67% accuracy on downstream tasks. |
On the Limitations of Language-targeted Pruning: Investigating the Calibration Language Impact in Multilingual LLM Pruning (2026.tacl-1)
Copied to clipboard
| Challenge: | Recent advances in large language model pruning have shown high predictive performance in post-training settings. |
| Approach: | They conduct an empirical study on the performance and internal representation changes associated with pruning multilingual models for monolingual applications. |
| Outcome: | The proposed pruning methods retain perplexity and yield high signal-to-noise ratios, but not consistently improve downstream tasks. |
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. |
Large Language Models with Controllable Working Memory (2023.findings-acl)
Copied to clipboard
Daliang Li, Ankit Singh Rawat, Manzil Zaheer, Xin Wang, Michal Lukasik, Andreas Veit, Felix Yu, Sanjiv Kumar
| Challenge: | Large language models (LLMs) have led to a series of breakthroughs in natural language processing due to the massive amounts of world knowledge they memorize during pretraining. |
| Approach: | They propose a method to inject counterfactual and irrelevant contexts into standard supervised datasets to strengthen both controllability and robustness. |
| Outcome: | The proposed method improves controllability and robustness across model architectures and sizes. |
Two-Stage Regularization-Based Structured Pruning for LLMs (2026.acl-long)
Copied to clipboard
Mingkuan Feng, Jinyang Wu, Siyuan Liu, Shuai Zhang, Hongjian Fang, Ruihan Jin, Feihu Che, Pengpeng Shao, Zhengqi Wen, Jianhua Tao
| Challenge: | Structural pruning is a promising solution for large language models . prior structured pruning methods remove unimportant parameters based on certain metrics . |
| Approach: | They propose a structural pruning method that iteratively learns the weights of transformer layers by adding their l1-norm to the loss function. |
| Outcome: | The proposed pruning method outperforms strong layer-wise pruning methods without requiring retraining. |
Pruning as a Domain-specific LLM Extractor (2024.findings-naacl)
Copied to clipboard
Nan Zhang, Yanchi Liu, Xujiang Zhao, Wei Cheng, Runxue Bao, Rui Zhang, Prasenjit Mitra, Haifeng Chen
| Challenge: | Large Language Models (LLMs) have exhibited remarkable proficiency across a wide array of NLP tasks. |
| Approach: | They propose a method for pruning large language models using general or task-specific weights to extract a compressed, task-agnostic LLM. |
| Outcome: | The proposed method extracts a compressed, domain-specific, and task- agnostic LLM by identifying LLM weights that are pivotal for general capabilities, like linguistic capability and multi-task solving, and domain- specific knowledge. |
Bypass Back-propagation: Optimization-based Structural Pruning for Large Language Models via Policy Gradient (2025.acl-long)
Copied to clipboard
| Challenge: | Recent pruning methods rely on heuristically hand-crafted metrics, leading to suboptimal performance. |
| Approach: | They propose a method that optimizes pruning masks by minimizing back-propagation . they learn an underlying Bernoulli distribution to sample binary pruning mask samples . |
| Outcome: | The proposed method is able to support global and heterogeneous pruning without back-propagation. |
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. |
ALPS: Attention Localization and Pruning Strategy for Efficient Adaptation of Large Language Models (2025.findings-acl)
Copied to clipboard
Hao Chen, Haoze Li, Zhiqing Xiao, Lirong Gao, Qi Zhang, Xiaomeng Hu, Ningtao Wang, Xing Fu, Junbo Zhao
| Challenge: | Prior research has focused on optimizing general-purpose large language models to downstream tasks . however, these approaches inherently introduce data dependency, which hinders generalization and reusability. |
| Approach: | They propose an algorithm that localizes the most task-sensitive attention heads and prunes by restricting attention training updates to these heads, thereby reducing alignment costs. |
| Outcome: | The proposed algorithm achieves 2% performance improvement over baselines on three tasks while localizing the most task-sensitive attention heads. |
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% . |