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Pre-trained large language models have shown successful progress in many language understanding benchmarks.
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 · 1901
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A survey of reinforcement learning informed by natural language
Jelena Luketina, Nantas Nardelli, Gregory Farquhar, Jakob Foerster, Jacob Andreas, Edward Grefenstette, Shimon Whiteson, and Tim Rocktäschel. 2019 · 1906
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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. 2019 · 1910
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Unified pragmatic models for generating and following instructions
Daniel Fried, Jacob Andreas, and Dan Klein. 2018 · 1963
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Enabling robots to understand incomplete natural language instructions using commonsense reasoning
Haonan Chen, Hao Tan, Alan Kuntz, Mohit Bansal, and Ron Alterovitz. 2020 · 1969
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Compositional generalization in semantic parsing: Pre-training vs. specialized architectures
Daniel Furrer, Marc van Zee, Nathan Scales, and Nathanael Schärli. 2020 · 2007
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Reinforcement learning for mapping instructions to actions
S.R.K. Branavan, Harr Chen, Luke Zettlemoyer, and Regina Barzilay. 2009 · 2009
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Learning to win by reading manuals in a monte-carlo framework
SRK Branavan, David Silver, and Regina Barzilay. 2012 · 2012
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Moca: A modular object-centric approach for interactive instruction following
Kunal Pratap Singh, Suvaansh Bhambri, Byeonghwi Kim, Roozbeh Mottaghi, and Jonghyun Choi. 2020 · 2012
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A diversity-promoting objective function for neural conversation models
Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and Bill Dolan. 2016 · 2016
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Mutual information and diverse decoding improve neural machine translation
Jiwei Li and Dan Jurafsky. 2016 · 2016
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Listen, attend, and walk: Neural mapping of navigational instructions to action sequences
Hongyuan Mei, Mohit Bansal, and Matthew R Walter. 2016 · 2016
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Ai2-thor: An interactive 3d environment for visual ai
Eric Kolve, Roozbeh Mottaghi, Winson Han, Eli VanderBilt, Luca Weihs, Alvaro Herrasti, Daniel Gordon, Yuke Zhu, Abhinav Gupta, and Ali Farhadi. 2017 · 2017
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Improving text-to-SQL evaluation methodology
Catherine Finegan-Dollak, Jonathan K. Kummerfeld, Li Zhang, Karthik Ramanathan, Sesh Sadasivam, Rui Zhang, and Dragomir Radev. 2018 · 2018
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Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks
Brenden Lake and Marco Baroni. 2018 · 2018
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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
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Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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Executing instructions in situated collaborative interactions
Alane Suhr, Claudia Yan, Jack Schluger, Stanley Yu, Hadi Khader, Marwa Mouallem, Iris Zhang, and Yoav Artzi. 2019 · 2019
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Unlocking compositional generalization in pre-trained models using intermediate representations
Jonathan Herzig, Peter Shaw, Ming-Wei Chang, Kelvin Guu, Panupong Pasupat, and Yuan Zhang. 2021 · 2021
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Implicit representations of meaning in neural language models
Belinda Z. Li, Maxwell Nye, and Jacob Andreas. 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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Language models are few-shot butlers
Vincent Micheli and Francois Fleuret. 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. 2021 · 2021
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Constrained language models yield few-shot semantic parsers
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Visually-grounded planning without vision: Language models infer detailed plans from high-level instructions
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Alfred: A benchmark for interpreting grounded instructions for everyday tasks
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On the opportunities and risks of foundation models
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