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Current methods for few-shot fine-tuning of pretrained masked language models (PLMs) require carefully engineered prompts and verbalizers for each new task to convert examples into a cloze-format that the PLM can score.
Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
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“cloze procedure”: A new tool for measuring readability
Wilson L Taylor. 1953 · 1953
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Wordnet: a lexical database for english
George A Miller. 1995 · 1995
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Building a question answering test collection
Ellen M Voorhees and Dawn M Tice. 2000 · 2000
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Fine-tuning pretrained language models: Weight initializations, data orders, and early stopping
Jesse Dodge, Gabriel Ilharco, Roy Schwartz, Ali Farhadi, Hannaneh Hajishirzi, and Noah Smith. 2020 · 2002
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Mining and summarizing customer reviews
Minqing Hu and Bing Liu. 2004 · 2004
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A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts
Bo Pang and Lillian Lee. 2004 · 2004
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The pascal recognising textual entailment challenge
Ido Dagan, Oren Glickman, and Bernardo Magnini. 2005 · 2005
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Automatically constructing a corpus of sentential paraphrases
William B Dolan and Chris Brockett. 2005 · 2005
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Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
Bo Pang and Lillian Lee. 2005 · 2005
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Verbnet: A broad-coverage, comprehensive verb lexicon
Karin Kipper Schuler. 2005 · 2005
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The second pascal recognising textual entailment challenge
Roy Bar-Haim, Ido Dagan, Bill Dolan, Lisa Ferro, and Danilo Giampiccolo. 2006 · 2006
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The third PASCAL recognizing textual entailment challenge
Danilo Giampiccolo, Bernardo Magnini, Ido Dagan, and Bill Dolan. 2007 · 2007
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The fifth pascal recognizing textual entailment challenge
Luisa Bentivogli, Ido Dagan, Hoa Trang Dang, Danilo Giampiccolo, and Bernardo Magnini. 2009 · 2009
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Y Ng, and Christopher Potts. 2013 · 2013
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Using machine learning algorithms for breast cancer risk prediction and diagnosis
Hiba Asri, Hajar Mousannif, Hassan Al Moatassime, and Thomas Noel. 2016 · 2016
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Gaussian error linear units (gelus)
Dan Hendrycks and Kevin Gimpel. 2016 · 2016
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Squad: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Hypernetworks
David Ha, Andrew Dai, and Quoc V. Le. 2017 · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel. 2017 · 2017
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Measuring the intrinsic dimension of objective landscapes
Chunyuan Li, Heerad Farkhoor, Rosanne Liu, and Jason Yosinski. 2018 · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
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Efficient parametrization of multi-domain deep neural networks
Sylvestre-Alvise Rebuffi, Hakan Bilen, and Andrea Vedaldi. 2018 · 2018
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The commitmentbank: Investigating projection in naturally occurring discourse
Marie-Catherine De Marneffe, Mandy Simons, and Judith Tonhauser. 2019 · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Revisiting few-sample bert fine-tuning
Tianyi Zhang, Felix Wu, Arzoo Katiyar, Kilian Q Weinberger, and Yoav Artzi. 2020 · 2020
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Intrinsic dimensionality explains the effectiveness of language model fine-tuning
Armen Aghajanyan, Luke Zettlemoyer, and Sonal Gupta. 2021 · 2021
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Making pre-trained language models better few-shot learners
Tianyu Gao, Adam Fisch, and Danqi Chen. 2021 · 2021
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How many data points is a prompt worth?
Teven Le Scao and Alexander M Rush. 2021 · 2021
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The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang. 2021 · 2021
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Parameter-efficient transfer learning for nlp
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. 2019 · 2019
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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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Wic: the word-in-context dataset for evaluating context-sensitive meaning representations
Mohammad Taher Pilehvar and Jose Camacho-Collados. 2019 · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2020
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Tinytl: Reduce memory, not parameters for efficient on-device learning
Han Cai, Chuang Gan, Ligeng Zhu, and Song Han. 2020 · 2020
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How can we know what language models know?
Zhengbao Jiang, Frank F Xu, Jun Araki, and Graham Neubig. 2020 · 2020
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Cutting down on prompts and parameters: Simple few-shot learning with language models
Robert L Logan IV, Ivana Balažević, Eric Wallace, Fabio Petroni, Sameer Singh, and Sebastian Riedel. 2021 · 2021
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Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity
Yao Lu, Max Bartolo, Alastair Moore, Sebastian Riedel, and Pontus Stenetorp. 2021 · 2021
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Noisy channel language model prompting for few-shot text classification
Sewon Min, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2021 · 2021
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On the stability of fine-tuning bert: Misconceptions, explanations, and strong baselines
Marius Mosbach, Maksym Andriushchenko, and Dietrich Klakow. 2021 · 2021
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True few-shot learning with language models
Ethan Perez, Douwe Kiela, and Kyunghyun Cho. 2021 · 2021
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AdapterFusion: Non-destructive task composition for transfer learning
Jonas Pfeiffer, Aishwarya Kamath, Andreas Rückĺe, Cho Kyunghyun, and Iryna Gurevych. 2021 · 2021
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Learning how to ask: Querying lms with mixtures of soft prompts
Guanghui Qin and Jason Eisner. 2021 · 2021
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Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked languagemodels
Shauli Ravfogel, Elad Ben-Zaken, and Yoav Goldberg. 2021 · 2021
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Improving and simplifying pattern exploiting training
Derek Tam, Rakesh R Menon, Mohit Bansal, Shashank Srivastava, and Colin Raffel. 2021 · 2021
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Do prompt-based models really understand the meaning of their prompts?
Albert Webson and Ellie Pavlick. 2021 · 2021
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Beyond fully-connected layers with quaternions: Parameterization of hypercomplex multiplications with 1/n parameters
Aston Zhang, Yi Tay, SHUAI Zhang, Alvin Chan, Anh Tuan Luu, Siu Hui, and Jie Fu. 2021 · 2021
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Calibrate before use: Improving few-shot performance of language models
Tony Z. Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh. 2021 · 2021
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