Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Abstract and 1. Introduction Background
2.1 Mixture-of-Experts
2.2 Adapters


Mixture-of-Adaptations
3.1 Routing Policy
3.2 Consistency regularization
3.3 Adaptation module merging and 3.4 Adaptation module sharing
3.5 Connection to Bayesian Neural Networks and Model Ensembling


Experiments
4.1 Experimental Setup
4.2 Key Results
4.3 Ablation Study


Related Work


Conclusions


Limitations


Acknowledgment and References Background
2.1 Mixture-of-Experts
2.2 Adapters Background Background Background 2.1 Mixture-of-Experts 2.1 Mixture-of-Experts 2.2 Adapters 2.2 Adapters Mixture-of-Adaptations
3.1 Routing Policy
3.2 Consistency regularization
3.3 Adaptation module merging and 3.4 Adaptation module sharing
3.5 Connection to Bayesian Neural Networks and Model Ensembling Mixture-of-Adaptations Mixture-of-Adaptations Mixture-of-Adaptations 3.1 Routing Policy 3.1 Routing Policy 3.2 Consistency regularization 3.2 Consistency regularization 3.3 Adaptation module merging and 3.4 Adaptation module sharing 3.3 Adaptation module merging and 3.4 Adaptation module sharing 3.5 Connection to Bayesian Neural Networks and Model Ensembling 3.5 Connection to Bayesian Neural Networks and Model Ensembling Experiments
4.1 Experimental Setup
4.2 Key Results
4.3 Ablation Study Experiments Experiments 4.1 Experimental Setup 4.1 Experimental Setup 4.2 Key Results 4.2 Key Results 4.3 Ablation Study 4.3 Ablation Study Related Work Related Work Related Work Related Work Conclusions Conclusions Conclusions Conclusions Limitations Limitations Limitations Limitations Acknowledgment and References Acknowledgment and References Acknowledgment and References Acknowledgment and References Appendix Appendix A. Few-shot NLU Datasets B. Ablation Study C. Detailed Results on NLU Tasks D. Hyper-parameter A. Few-shot NLU Datasets B. Ablation Study C. Detailed Results on NLU Tasks D. Hyper-parameter 5 Related Work Parameter-efficient fine-tuning of PLMs. Recent works on parameter-efficient fine-tuning (PEFT) can be roughly categorized into two Parameter-efficient fine-tuning of PLMs. categories: (1) tuning a subset of existing parameters including head fine-tuning (Lee et al., 2019), bias term tuning (Zaken et al., 2021), (2) tuning newly-introduced parameters including adapters (Houlsby et al., 2019; Pfeiffer et al., 2020), prompt-tuning (Lester et al., 2021), prefixtuning (Li and Liang, 2021) and low-rank adaptation (Hu et al., 2021). As opposed to prior works operating on a single adaptation module, AdaMix introduces a mixture of adaptation modules with stochastic routing during training and adaptation module merging during inference to keep the same computational cost as with a single module. Further, AdaMix can be used on top of any PEFT method to further boost its performance. Mixture-of-Expert (MoE). Shazeer et al., 2017 introduced the MoE model with a single gating network with T op-k routing and load balancing across experts. Fedus et al., 2021 propose initialization and training schemes for T op-1 routing. Zuo et al., 2021 propose consistency regularization for random routing; Yang et al., 2021 propose k T op-1 routing with expert-prototypes, and Roller et al., 2021; Lewis et al., 2021 address other load balancing issues. All the above works study sparse MoE with pre-training the entire model from scratch. In contrast, we study parameter-efficient adaptation of pre-trained language models by tuning only a very small number of sparse adapter parameters. Mixture-of-Expert (MoE). Averaging model weights. Recent explorations (Szegedy et al., 2016; Matena and Raffel, 2021; Wortsman et al., 2022; Izmailov et al., 2018) study model aggregation by averaging all the model weights. (Matena and Raffel, 2021) propose to merge pre-trained language models which are fine-tuned on various text classification tasks. (Wortsman et al., 2022) explores averaging model weights from various independent runs on the same task with different hyper-parameter configurations. In contrast to the above works on full model finetuning, we focus on parameter-efficient fine-tuning. We explore weight averaging for merging weights of adaptation modules consisting of small tunable parameters that are updated during model tuning while keeping the large model parameters fixed. Averaging model weights. 6 Conclusions We develop a new framework AdaMix for parameter-efficient fine-tuning (PEFT) of large pretrained language models (PLM). AdaMix leverages a mixture of adaptation modules to improve downstream task performance without increasing the computational cost (e.g., FLOPs, parameters) of the underlying adaptation method. We demonstrate AdaMix to work with and improve over different PEFT methods like adapters and low rank decompositions across NLU and NLG tasks. By tuning only 0.1 − 0.2% of PLM parameters, AdaMix outperforms full model fine-tuning that updates all the model parameters as well as other state-of-the-art PEFT methods. 7 Limitations The proposed AdaMix method is somewhat compute-intensive as it involves fine-tuning largescale language models. The training cost of the proposed AdaMix is higher than standard PEFT methods since the training procedure involves multiple copies of adapters. Based on our empirical observation, the number of training iterations for AdaMix is usually between 1∼2 times the training for standard PEFT methods. This imposes negative impact on carbon footprint from training the described models. AdaMix is orthogonal to most of the existing parameter-efficient fine-tuning (PEFT) studies and is able to potentially improve the performance of any PEFT method. 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Tianyi Zhang, Felix Wu, Arzoo Katiyar, Kilian Q Weinberger, and Yoav Artzi. 2021. Revisiting fewsample BERT fine-tuning. Simiao Zuo, Xiaodong Liu, Jian Jiao, Young Jin Kim, Hany Hassan, Ruofei Zhang, Tuo Zhao, and Jianfeng Gao. 2021. Taming sparsely activated transformer with stochastic experts. arXiv preprint arXiv:2110.04260. Authors:
(1) Yaqing Wang, Purdue University (wang5075@purdue.edu);
(2) Sahaj Agarwal, Microsoft (sahagar@microsoft.com);
(3) Subhabrata Mukherjee, Microsoft Research (submukhe@microsoft.com);
(4) Xiaodong Liu, Microsoft Research (xiaodl@microsoft.com);
(5) Jing Gao, Purdue University (jinggao@purdue.edu);
(6) Ahmed Hassan Awadallah, Microsoft Research (hassanam@microsoft.com);
(7) Jianfeng Gao, Microsoft Research (jfgao@microsoft.com). Authors: Authors: (1) Yaqing Wang, Purdue University (wang5075@purdue.edu); (2) Sahaj Agarwal, Microsoft (sahagar@microsoft.com); (3) Subhabrata Mukherjee, Microsoft Research (submukhe@microsoft.com); (4) Xiaodong Liu, Microsoft Research (xiaodl@microsoft.com); (5) Jing Gao, Purdue University (jinggao@purdue.edu); (6) Ahmed Hassan Awadallah, Microsoft Research (hassanam@microsoft.com); (7) Jianfeng Gao, Microsoft Research (jfgao@microsoft.com). This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv available on arxiv

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Beating Full Fine-Tuning with Just 0.2% of Parameters

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