Papers with Low-rank decomposition
Beyond Uniform SVD: Dual-Level Optimization across Columns and Modules for LLM Compression (2026.findings-acl)
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| Challenge: | Existing methods for low-rank decomposition overlook decomposing errors and suboptimal approximation. |
| Approach: | They propose a low-rank decomposition framework that integrates low-level optimization at column and module levels. |
| Outcome: | The proposed framework outperforms state-of-the-art methods and baselines in SVD and pruning. |
Break Through the Compression Bottleneck: From Theory to Practice (2026.findings-acl)
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| Challenge: | Existing compression methods suffer from bottleneck issues when compression ratio is increased. |
| Approach: | They propose a novel approach to combine low-rank decomposition and quantization methods to reduce the compression bottleneck. |
| Outcome: | The proposed method reduces the computational and memory overhead of existing methods while maintaining model accuracy. |