Papers with semi-structured pruning
Beyond Perplexity: Multi-dimensional Safety Evaluation of LLM Compression (2024.findings-emnlp)
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| Challenge: | Prior work on compression prioritizes preserving perplexity, which is analogous to training loss. |
| Approach: | They examine the impact of model compression along four dimensions: degeneration harm, representational harm, dialect bias, and language modeling and downstream task performance. |
| Outcome: | The proposed compression methods can lead to unexpected consequences, the authors show . quantization preserves bias while pruning degrades quickly. |
FISTAPruner: Layer-wise Post-training Pruning for Large Language Models (2025.emnlp-main)
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| Challenge: | Existing pruning methods require inefficient retraining for billion-scale LLMs or rely on heuristicically designed metrics to determine pruning masks, leading to performance degradation. |
| Approach: | They propose a convex optimization model that induces sparsity in large language models by leveraging FISTA. |
| Outcome: | The proposed method can remove 50% of model parameters while retaining 98.6% and 95.6% of the zero-shot performance. |