Enabling Lightweight Fine-tuning for Pre-trained Language Model Compression based on Matrix Product Operators
This work addresses the computational cost of fine-tuning large language models, offering a domain-specific solution for efficient model compression.
The paper tackles the problem of compressing pre-trained language models by introducing a matrix product operator (MPO) decomposition method, which reduces the parameters needing fine-tuning by 91% on average while maintaining effectiveness.
This paper presents a novel pre-trained language models (PLM) compression approach based on the matrix product operator (short as MPO) from quantum many-body physics. It can decompose an original matrix into central tensors (containing the core information) and auxiliary tensors (with only a small proportion of parameters). With the decomposed MPO structure, we propose a novel fine-tuning strategy by only updating the parameters from the auxiliary tensors, and design an optimization algorithm for MPO-based approximation over stacked network architectures. Our approach can be applied to the original or the compressed PLMs in a general way, which derives a lighter network and significantly reduces the parameters to be fine-tuned. Extensive experiments have demonstrated the effectiveness of the proposed approach in model compression, especially the reduction in finetuning parameters (91% reduction on average).