Scaling Pre-trained Language Models to Deeper via Parameter-efficient Architecture

• URL: http://arxiv.org/abs/2303.16753v2
• Date: Tue, 11 Apr 2023 02:45:10 GMT
• Title: Scaling Pre-trained Language Models to Deeper via Parameter-efficient Architecture
• Authors: Peiyu Liu, Ze-Feng Gao, Yushuo Chen, Wayne Xin Zhao, Ji-Rong Wen
• Abstract summary: We design a more capable parameter-sharing architecture based on matrix product operator (MPO) MPO decomposition can reorganize and factorize the information of a parameter matrix into two parts. Our architecture shares the central tensor across all layers for reducing the model size.
• Score: 68.13678918660872
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: In this paper, we propose a highly parameter-efficient approach to scaling pre-trained language models (PLMs) to a deeper model depth. Unlike prior work that shares all parameters or uses extra blocks, we design a more capable parameter-sharing architecture based on matrix product operator (MPO). MPO decomposition can reorganize and factorize the information of a parameter matrix into two parts: the major part that contains the major information (central tensor) and the supplementary part that only has a small proportion of parameters (auxiliary tensors). Based on such a decomposition, our architecture shares the central tensor across all layers for reducing the model size and meanwhile keeps layer-specific auxiliary tensors (also using adapters) for enhancing the adaptation flexibility. To improve the model training, we further propose a stable initialization algorithm tailored for the MPO-based architecture. Extensive experiments have demonstrated the effectiveness of our proposed model in reducing the model size and achieving highly competitive performance.

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