Fetching the paper…
Reading the bibliography…
The recent GPT-3 model (Brown et al., 2020) achieves remarkable few-shot performance solely by leveraging a natural-language prompt and a few task demonstrations as input context.
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.
Building a question answering test collection
Ellen M Voorhees and Dawn M Tice. 2000 · 2000
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 Smith. 2020 · 2002
Earlier work this paper cites.
Longformer: The long-document Transformer
Iz Beltagy, Matthew E. Peters, and Arman Cohan. 2020 · 2004
Earlier work this paper cites.
Mining and summarizing customer reviews
Minqing Hu and Bing Liu. 2004 · 2004
Earlier work this paper cites.
A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts
Bo Pang and Lillian Lee. 2004 · 2004
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.
Automatically constructing a corpus of sentential paraphrases
William B. Dolan and Chris Brockett. 2005 · 2005
Earlier work this paper cites.
Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
Bo Pang and Lillian Lee. 2005 · 2005
Earlier work this paper cites.
Annotating expressions of opinions and emotions in language
Janyce Wiebe, Theresa Wilson, and Claire Cardie. 2005 · 2005
Earlier work this paper cites.
The second PASCAL recognising textual entailment challenge
Roy Bar Haim, Ido Dagan, Bill Dolan, Lisa Ferro, Danilo Giampiccolo, Bernardo Magnini, and Idan Szpektor. 2006 · 2006
Earlier work this paper cites.
The third PASCAL recognizing textual entailment challenge
Danilo Giampiccolo, Bernardo Magnini, Ido Dagan, and Bill Dolan. 2007 · 2007
Earlier work this paper cites.
The fifth PASCAL recognizing textual entailment challenge
Luisa Bentivogli, Peter Clark, Ido Dagan, and Danilo Giampiccolo. 2009 · 2009
Earlier work this paper cites.
Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Ng, and Christopher Potts. 2013 · 2013
Earlier work this paper cites.
A large annotated corpus for learning natural language inference
Samuel Bowman, Gabor Angeli, Christopher Potts, and Christopher D Manning. 2015 · 2015
Earlier work this paper cites.
SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
Earlier work this paper cites.
SemEval-2017 task 1: Semantic textual similarity multilingual and crosslingual focused evaluation
Daniel Cer, Mona Diab, Eneko Agirre, Iñigo Lopez-Gazpio, and Lucia Specia. 2017 · 2017
Cited alongside, same era.
Adversarial training methods for semi-supervised text classification
Takeru Miyato, Andrew M Dai, and Ian Goodfellow. 2017 · 2017
Cited alongside, same era.
Fewrel: A large-scale supervised few-shot relation classification dataset with state-of-the-art evaluation
Xu Han, Hao Zhu, Pengfei Yu, Ziyun Wang, Yuan Yao, Zhiyuan Liu, and Maosong Sun. 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.
Sentence encoders on STILTs: Supplementary training on intermediate labeled-data tasks
Jason Phang, Thibault Févry, and Samuel R Bowman. 2018 · 2018
Cited alongside, same era.
GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman. 2019 · 2019
Later among the works it cites.
Neural network acceptability judgments
Alex Warstadt, Amanpreet Singh, and Samuel R. Bowman. 2019 · 2019
Later among the works it cites.
Few-shot text classification with distributional signatures
Yujia Bao, Menghua Wu, Shiyu Chang, and Regina Barzilay. 2020 · 2020
Closest in time.
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, et al. 2020 · 2020
Closest in time.
MixText: Linguistically-informed interpolation of hidden space for semi-supervised text classification
Jiaao Chen, Zichao Yang, and Diyi Yang. 2020 · 2020
Closest in time.
How can we know what language models know?
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
Cited alongside, same era.
A simple method for commonsense reasoning
Trieu H Trinh and Quoc V Le. 2018 · 2018
Cited alongside, same era.
A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman. 2018 · 2018
Cited alongside, same era.
Diverse few-shot text classification with multiple metrics
Mo Yu, Xiaoxiao Guo, Jinfeng Yi, Shiyu Chang, Saloni Potdar, Yu Cheng, Gerald Tesauro, Haoyu Wang, and Bowen Zhou. 2018 · 2018
Cited alongside, same era.
Commonsense knowledge mining from pretrained models
Joe Davison, Joshua Feldman, and Alexander M Rush. 2019 · 2019
Cited alongside, same era.
BERT: Pre-training of deep bidirectional Transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Hyperspherical prototype networks
Pascal Mettes, Elise van der Pol, and Cees Snoek. 2019 · 2019
Cited alongside, same era.
Zhengbao Jiang, Frank F Xu, Jun Araki, and Graham Neubig. 2020 · 2020
Closest in time.
Mixout: Effective regularization to finetune large-scale pretrained language models
Cheolhyoung Lee, Kyunghyun Cho, and Wanmo Kang. 2020 · 2020
Closest in time.
Exploring the limits of transfer learning with a unified text-to-text Transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020 · 2020
Closest in time.
Automatically identifying words that can serve as labels for few-shot text classification
Timo Schick, Helmut Schmid, and Hinrich Schütze. 2020 · 2020
Closest in time.
AutoPrompt: Automatic prompt construction for masked language models
Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, and Sameer Singh. 2020 · 2020
Closest in time.
oLMpics-on what language model pre-training captures
Alon Talmor, Yanai Elazar, Yoav Goldberg, and Jonathan Berant. 2020 · 2020
Closest in time.
Unsupervised data augmentation for consistency training
Qizhe Xie, Zihang Dai, Eduard Hovy, Thang Luong, and Quoc Le. 2020 · 2020
Closest in time.
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
Closest in time.
Revisiting few-sample BERT fine-tuning
Tianyi Zhang, Felix Wu, Arzoo Katiyar, Kilian Q Weinberger, and Yoav Artzi. 2021 · 2021
Closest in time.
Factual probing is [MASK]: Learning vs. learning to recall
Zexuan Zhong, Dan Friedman, and Danqi Chen. 2021 · 2021
Closest in time.