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        <title>Parameter-Efficient Mixture-of-Experts Architecture for Pre-trained Language Models</title>
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    <abstract>Recently, Mixture-of-Experts (short as MoE) architecture has achieved remarkable success in increasing the model capacity of large-scale language models. However, MoE requires incorporating significantly more parameters than the base model being extended. In this paper, we propose building a parameter-efficient MoE architecture by sharing information across experts. We adopt matrix product operator (MPO, a tensor decomposition from quantum many-body physics) to reconstruct the parameter matrix in the expert layer and increase model capacity for pre-trained language models by sharing parameters of the central tensor (containing the core information) among different experts while enabling the specificity through the auxiliary tensors (complementing the central tensor) of different experts. To address the unbalanced optimization issue, we further design the gradient mask strategy for the MPO-based MoE architecture. Extensive experiments based on T5 and GPT-2 show improved performance and efficiency of the pre-trained language model (27.2x reduction in total parameters for the superior model performance, compared with the Switch Transformers). Our code is publicly available at https://github.com/RUCAIBox/MPO/MPOE.</abstract>
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%0 Conference Proceedings
%T Parameter-Efficient Mixture-of-Experts Architecture for Pre-trained Language Models
%A Gao, Ze-Feng
%A Liu, Peiyu
%A Zhao, Wayne Xin
%A Lu, Zhong-Yi
%A Wen, Ji-Rong
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F gao-etal-2022-parameter
%X Recently, Mixture-of-Experts (short as MoE) architecture has achieved remarkable success in increasing the model capacity of large-scale language models. However, MoE requires incorporating significantly more parameters than the base model being extended. In this paper, we propose building a parameter-efficient MoE architecture by sharing information across experts. We adopt matrix product operator (MPO, a tensor decomposition from quantum many-body physics) to reconstruct the parameter matrix in the expert layer and increase model capacity for pre-trained language models by sharing parameters of the central tensor (containing the core information) among different experts while enabling the specificity through the auxiliary tensors (complementing the central tensor) of different experts. To address the unbalanced optimization issue, we further design the gradient mask strategy for the MPO-based MoE architecture. Extensive experiments based on T5 and GPT-2 show improved performance and efficiency of the pre-trained language model (27.2x reduction in total parameters for the superior model performance, compared with the Switch Transformers). Our code is publicly available at https://github.com/RUCAIBox/MPO/MPOE.
%U https://aclanthology.org/2022.coling-1.288
%P 3263-3273

Markdown (Informal)

[Parameter-Efficient Mixture-of-Experts Architecture for Pre-trained Language Models](https://aclanthology.org/2022.coling-1.288) (Gao et al., COLING 2022)

• Parameter-Efficient Mixture-of-Experts Architecture for Pre-trained Language Models (Gao et al., COLING 2022)

ACL

• Ze-Feng Gao, Peiyu Liu, Wayne Xin Zhao, Zhong-Yi Lu, and Ji-Rong Wen. 2022. Parameter-Efficient Mixture-of-Experts Architecture for Pre-trained Language Models. In Proceedings of the 29th International Conference on Computational Linguistics, pages 3263–3273, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.