Enhancing Scalability of Pre-trained Language Models via Efficient Parameter Sharing
Submission Type: Regular Long Paper
Submission Track: Efficient Methods for NLP
Keywords: parameter-efficient, pre-trained language models, scalability
TL;DR: In this paper, we propose a highly parameter-efficient approach to scaling pre-trained language models to a deeper model depth.
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), an efficient tensor decomposition method to factorize the parameter matrix into a set of local tensors. Based on such a decomposition, we share the important local tensor across all layers for reducing the model size and meanwhile keep layer-specific 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 enhancing scalability and achieving higher performance (i.e., with fewer parameters than BERT-base, we successfully scale the model depth by a factor of 4x and even achieve 0.1 points higher than BERT-large for GLUE score). The code to reproduce the results of this paper can be found at https://github.com/RUCAIBox/MPOBERT-code.
Submission Number: 1784
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