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State-of-the-art parameter-efficient fine-tuning methods rely on introducing adapter modules between the layers of a pretrained language model.
Massively multilingual neural machine translation in the wild: Findings and challenges
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Tushar Khot, Ashish Sabharwal, and Peter Clark. 2018 · 2018
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Ethan Perez, Florian Strub, Harm De Vries, Vincent Dumoulin, and Aaron Courville. 2018 · 2018
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Jason Phang, Thibault Févry, and Samuel R Bowman. 2018 · 2018
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To tune or not to tune? adapting pretrained representations to diverse tasks
Matthew E Peters, Sebastian Ruder, and Noah A Smith. 2019 · 2019
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Bert and pals: Projected attention layers for efficient adaptation in multi-task learning
Asa Cooper Stickland and Iain Murray. 2019 · 2019
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Alex Warstadt, Amanpreet Singh, and Samuel R. Bowman. 2019 · 2019
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Learning to Few-Shot Learn Across Diverse Natural Language Classification Tasks
Trapit Bansal, Rishikesh Jha, and Andrew McCallum. 2020 · 2020
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Continual learning with hypernetworks
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Simple, scalable adaptation for neural machine translation
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AdapterHub: A framework for adapting transformers
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Exploring the limits of transfer learning with a unified text-to-text transformer
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Ahmet Üstün, Arianna Bisazza, Gosse Bouma, and Gertjan van Noord. 2020 · 2020
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Exploring and predicting transferability across NLP tasks
Tu Vu, Tong Wang, Tsendsuren Munkhdalai, Alessandro Sordoni, Adam Trischler, Andrew Mattarella-Micke, Subhransu Maji, and Mohit Iyyer. 2020 · 2020
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Universal natural language processing with limited annotations: Try few-shot textual entailment as a start
Wenpeng Yin, Nazneen Fatema Rajani, Dragomir Radev, Richard Socher, and Caiming Xiong. 2020 · 2020
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Hypergrid transformers: Towards a single model for multiple tasks
Yi Tay, Zhe Zhao, Dara Bahri, Don Metzler, and Da-Cheng Juan. 2021 · 2021
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Revisiting few-sample bert fine-tuning
Tianyi Zhang, Felix Wu, Arzoo Katiyar, Kilian Q Weinberger, and Yoav Artzi. 2021 · 2021
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