Feature-based Low-Rank Compression of Large Language Models via Bayesian Optimization
<|reference_start|>Feature-based Low-Rank Compression of Large Language Models via Bayesian Optimization: In recent years, large language models (LLMs) have driven advances in natural language processing. Still, their growing scale has increased the computational burden, necessitating a balance between efficiency and performance. Low-rank compression, a promising technique, reduces non-essential parameters by decomposing weight matrices into products of two low-rank matrices. Yet, its application in LLMs has not been extensively studied. The key to low-rank compression lies in low-rank factorization and low-rank dimensions allocation. To address the challenges of low-rank compression in LLMs, we conduct empirical research on the low-rank characteristics of large models. We propose a low-rank compression method suitable for LLMs. This approach involves precise estimation of feature distributions through pooled covariance matrices and a Bayesian optimization strategy for allocating low-rank dimensions. Experiments on the LLaMA-2 models demonstrate that our method outperforms existing strong structured pruning and low-rank compression techniques in maintaining model performance at the same compression ratio.<|reference_end|>
@article{ji2024feature-based,
    title={Feature-based Low-Rank Compression of Large Language Models via Bayesian
  Optimization},
    author={Yixin Ji, Yang Xiang, Juntao Li, Wei Chen, Zhongyi Liu, Kehai Chen,
  Min Zhang},
    journal={arXiv preprint arXiv:2405.10616},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.10616},
    primaryClass={cs.CL cs.LG}
}