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 A Few-shot NLU Datasets Data. In contrast to the fully supervised setting in the above experiments, we also perform fewshot experiments following the prior study (Wang et al., 2021) on six tasks including MNLI (Williams et al., 2018), RTE (Dagan et al., 2005; Bar Haim et al., 2006; Giampiccolo et al., 2007; Bentivogli et al., 2009), QQP[1] and SST-2 (Socher et al.). The results are reported on their development set following (Zhang et al., 2021). MPQA (Wiebe et al., 2005) and Subj (Pang and Lee, 2004) are used for polarity and subjectivity detection, where we follow (Gao et al., 2021) to keep 2, 000 examples for testing. The few-shot model only has access to |K| labeled samples for any task. Following true few-shot learning setting (Perez et al., 2021; Wang et al., 2021), we do not use any additional validation set for any hyper-parameter tuning or early stopping. The performance of each model is reported after fixed number of training epochs. For a fair comparison, we use the same set of few-shot labeled instances for training as in (Wang et al., 2021). We train each model with 5 different seeds and report average performance with standard deviation across the runs. In the few-shot experiments, we follow (Wang et al., 2021) to train AdaMix via the prompt-based fine-tuning strategy. In contrast to (Wang et al., 2021), we do not use any unlabeled data. Data B Ablation Study C Detailed Results on NLU Tasks The results on NLU tasks are included in Table 1 and Table 13. The performance AdaMix with RoBERTa-large encoder achieves the best performance in terms of different task metrics in the GLUE benchmark. AdaMix with adapters is the only PEFT method which outperforms full model fine-tuning on all the tasks and on average score. Additionally, the improvement brought by AdaMix is more significant with BERT-base as the encoder, demonstrating 2.2% and 1.2% improvement over the performance of full model fine-tuning and the best performing baseline UNIPELT with BERTbase. The improvement is observed to be consistent as that with RoBERTa-large on every task. The NLG results are included in Table 4 and 5. D Hyper-parameter Detailed hyper-parameter configuration for different tasks presented in Table 15 and Table 16. 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 [1] https://www.quora.com/q/quoradata/

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Smarter AI Training with Few-Shot Natural Language Tasks

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