Papers with compressing models
Decomposed Trust: Privacy, Adversarial Robustness, Ethics, and Fairness in Low-Rank LLMs (2026.findings-acl)
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| Challenge: | Large language models (LLMs) have driven major advances across domains, yet their massive size hinders deployment in resource-constrained settings. |
| Approach: | They propose to compress large language models to reduce computation and memory consumption while maintaining accuracy. |
| Outcome: | The proposed algorithms preserve training data privacy but weaken the protection of personally identifiable information during conversations. |
Hierarchical Safety Realignment: Lightweight Restoration of Safety in Pruned Large Vision-Language Models (2025.findings-acl)
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| Challenge: | Recent work has shown that pruning can reduce model performance, but it can also lead to degradation in safety performance. |
| Approach: | They propose a hierarchical safety realignment approach to prune large vision-Language Models . they quantify contribution of each attention head to safety and restore neurons . |
| Outcome: | The proposed approach achieves significant safety improvements in LVLMs pruned post pruning. |
A Comparative Study on the Impact of Model Compression Techniques on Fairness in Language Models (2023.acl-long)
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| Challenge: | Existing literature demonstrates that compressing deep learning models could affect their fairness. |
| Approach: | They evaluate pruned, distilled, and quantized language models to assess their fairness . they also examine the impact of using multilingual models and evaluation measures . |
| Outcome: | The proposed methods can reduce the fairness of language models by reducing their complexity and reducing the cost of training and deployment. |