Fetching the paper…
Reading the bibliography…
The few-shot natural language understanding (NLU) task has attracted much recent attention.
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
Earlier work this paper cites.
Optimal cross-validation in density estimation with the l 2 l^{2} -loss
Alain Celisse. 2014 · 1910
Earlier work this paper cites.
Bagging predictors
Leo Breiman. 1996 · 1996
Earlier work this paper cites.
Fine-tuning pretrained language models: Weight initializations, data orders, and early stopping
Jesse Dodge, Gabriel Ilharco, Roy Schwartz, Ali Farhadi, Hannaneh Hajishirzi, and Noah A. Smith. 2020 · 2002
Earlier work this paper cites.
Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared 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 M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher 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 · 2005
Earlier work this paper cites.
The pascal recognising textual entailment challenge
Ido Dagan, Oren Glickman, and Bernardo Magnini. 2005 · 2005
Earlier work this paper cites.
Revisiting few-sample BERT fine-tuning
Tianyi Zhang, Felix Wu, Arzoo Katiyar, Kilian Q. Weinberger, and Yoav Artzi. 2020 · 2006
Earlier work this paper cites.
Autoprompt: Eliciting knowledge from language models with automatically generated prompts
Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, and Sameer Singh. 2020 · 2010
Earlier work this paper cites.
Choice of plausible alternatives: An evaluation of commonsense causal reasoning
Melissa Roemmele, Cosmin Adrian Bejan, and Andrew S Gordon. 2011 · 2011
Earlier work this paper cites.
Making pre-trained language models better few-shot learners
Tianyu Gao, Adam Fisch, and Danqi Chen. 2020 · 2012
Earlier work this paper cites.
The winograd schema challenge
Hector Levesque, Ernest Davis, and Leora Morgenstern. 2012 · 2012
Cited alongside, same era.
Practical bayesian optimization of machine learning algorithms
Jasper Snoek, Hugo Larochelle, and Ryan P Adams. 2012 · 2012
Cited alongside, same era.
BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Cited alongside, same era.
Universal Language Model Fine-tuning for Text Classification
Jeremy Howard and Sebastian Ruder. 2018 · 2018
Cited alongside, same era.
Looking beyond the surface: A challenge set for reading comprehension over multiple sentences
Daniel Khashabi, Snigdha Chaturvedi, Michael Roth, Shyam Upadhyay, and Dan Roth. 2018 · 2018
Cited alongside, same era.
The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
Closest in time.
Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang. 2021 · 2021
Closest in time.
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
Closest in time.
Improving and simplifying pattern exploiting training
Rakesh R. Menon, Mohit Bansal, Shashank Srivastava, and Colin Raffel. 2021 · 2021
Closest in time.
CLUES: few-shot learning evaluation in natural language understanding
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Mohammad Taher Pilehvar and José Camacho-Collados. 2018 · 2018
Cited alongside, same era.
Boolq: Exploring the surprising difficulty of natural yes/no questions
Christopher Clark, Kenton Lee, Ming-Wei Chang, Tom Kwiatkowski, Michael Collins, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
The commitmentbank: Investigating projection in naturally occurring discourse
Marie-Catherine De Marneffe, Mandy Simons, and Judith Tonhauser. 2019 · 2019
Cited alongside, same era.
Self-training with noisy student improves imagenet classification
Qizhe Xie, Minh-Thang Luong, Eduard H. Hovy, and Quoc V. Le. 2020 · 2020
Cited alongside, same era.
FLEX: unifying evaluation for few-shot NLP
Jonathan Bragg, Arman Cohan, Kyle Lo, and Iz Beltagy. 2021 · 2021
Cited alongside, same era.
What makes good in-context examples for gpt-3?
Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, and Weizhu Chen. 2021a
Cited in the paper.
Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding, Yujie Qian, Zhilin Yang, and Jie Tang. 2021b
Cited in the paper.
Subhabrata Mukherjee, Xiaodong Liu, Guoqing Zheng, Saghar Hosseini, Hao Cheng, Greg Yang, Christopher Meek, Ahmed Hassan Awadallah, and Jianfeng Gao. 2021 · 2021
Closest in time.
True few-shot learning with language models
Ethan Perez, Douwe Kiela, and Kyunghyun Cho. 2021 · 2021
Closest in time.
Free lunch for few-shot learning: Distribution calibration
Shuo Yang, Lu Liu, and Min Xu. 2021 · 2021
Closest in time.
Crossfit: A few-shot learning challenge for cross-task generalization in nlp
Qinyuan Ye, Bill Yuchen Lin, and Xiang Ren. 2021 · 2021
Closest in time.
Calibrate before use: Improving few-shot performance of language models
Tony Z. Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh. 2021 · 2021
Closest in time.
Flipda: Effective and robust data augmentation for few-shot learning
Jing Zhou, Yanan Zheng, Jie Tang, Jian Li, and Zhilin Yang. 2021 · 2021
Closest in time.