Papers by Zheyang Li
1+1>2: A Synergistic Sparse and Low-Rank Compression Method for Large Language Models (2025.findings-emnlp)
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| Challenge: | Low-rank approximation compresses the model by retaining its essential structure with minimal information loss. |
| Approach: | They propose a method that leverages the strengths of pruning and low-rank approximation for LLMs. |
| Outcome: | The proposed methods surpass the existing methods on LLaMA and Qwen2.5 models. |
Beyond Dynamic Quantization: An Efficient Static Hierarchical Mix-precision Framework for Near-Lossless LLM Compression (2025.emnlp-industry)
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| Challenge: | Existing methods for dynamic quantization are hardware-unfriendly and often lead to large quantization errors in static scenarios. |
| Approach: | They propose a Static Hierarchical Mix-precision Quantization method which quantifies both inter-layer and intra-layer sensitivity through unified derivations involving Hessian. |
| Outcome: | The proposed method achieves 75.58% on zero-shot reasoning tasks while yielding average speedup of 2.86. |