Papers by Guangxin Wu
Iterative Structured Pruning for Large Language Models with Multi-Domain Calibration (2026.eacl-industry)
Copied to clipboard
| Challenge: | Existing models with unstructured pruning often yield irregular sparsity patterns that necessitate specialized hardware or software support. |
| Approach: | They propose a structured pruning framework that eliminates entire architectural components and maintains compatibility with standard hardware accelerators. |
| Outcome: | The proposed model pruning framework achieves significant compression with minimal performance degradation on multiple models across diverse downstream tasks. |