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).
@article{arxiv.2106.02205,
title = {Enabling Lightweight Fine-tuning for Pre-trained Language Model Compression based on Matrix Product Operators},
author = {Peiyu Liu and Ze-Feng Gao and Wayne Xin Zhao and Z. Y. Xie and Zhong-Yi Lu and Ji-Rong Wen},
journal= {arXiv preprint arXiv:2106.02205},
year = {2021}
}