Enabling Lightweight Fine-tuning for Pre-trained Language Model Compression based on Matrix Product Operators

• URL: http://arxiv.org/abs/2106.02205v1
• Date: Fri, 4 Jun 2021 01:50:15 GMT
• Title: Enabling Lightweight Fine-tuning for Pre-trained Language Model Compression based on Matrix Product Operators
• Authors: Peiyu Liu, Ze-Feng Gao, Wayne Xin Zhao, Z.Y. Xie, Zhong-Yi Lu, Ji-Rong Wen
• Abstract summary: We present a novel pre-trained language models (PLM) compression approach based on the matrix product operator (short as MPO) from quantum many-body physics. 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.
• Score: 31.461762905053426
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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).

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