Structured Prompt Tuning

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.

Adafactor: Adaptive learning rates with sublinear memory cost

Noam Shazeer and Mitchell Stern. 2018 · 2018

Earlier work this paper cites.

BERT: Pre-training of deep bidirectional transformers for language understanding

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019

Earlier work this paper cites.

Roberta: A robustly optimized bert pretraining approach

Original

Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 2019

Earlier work this paper cites.

Decoupled weight decay regularization

Ilya Loshchilov and Frank Hutter. 2019 · 2019

Earlier work this paper cites.

Pytorch: An imperative style, high-performance deep learning library

Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019 · 2019

Earlier work this paper cites.

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

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 · 2020

Cited alongside, same era.

BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension

Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020 · 2020

Cited alongside, same era.

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

Cited alongside, same era.

AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts

Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, and Sameer Singh. 2020 · 2020

Cited alongside, same era.

The power of scale for parameter-efficient prompt tuning

Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021

Later among the works it cites.

Prefix-tuning: Optimizing continuous prompts for generation

Xiang Lisa Li and Percy Liang. 2021 · 2021

Later among the works it cites.

Learning how to ask: Querying LMs with mixtures of soft prompts

Guanghui Qin and Jason Eisner. 2021 · 2021

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It’s not just size that matters: Small language models are also few-shot learners

Timo Schick and Hinrich Schütze. 2021b · 2021

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Factual probing is [MASK]: Learning vs. learning to recall

Zexuan Zhong, Dan Friedman, and Danqi Chen. 2021 · 2021

Later among the works it cites.

Standing on the shoulders of giant frozen language models

Yoav Levine, Itay Dalmedigos, Ori Ram, Yoel Zeldes, Daniel Jannai, Dor Muhlgay, Yoni Osin, Opher Lieber, Barak Lenz, Shai Shalev-Shwartz, Amnon Shashua, Kevin Leyton-Brown, and Yoav Shoham. 2022 · 2022