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Pretrained language models often do not perform tasks in ways that are in line with our preferences, e.g., generating offensive text or factually incorrect summaries.
Language models are few-shot learners
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Learning from dialogue after deployment: Feed yourself, chatbot!
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The curious case of neural text degeneration
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Fine-tuning language models from human preferences
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ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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Realtoxicityprompts: Evaluating neural toxic degeneration in language models
Samuel Gehman, Suchin Gururangan, Maarten Sap, Yejin Choi, and Noah A Smith. 2020 · 2009
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Dialogue learning with human-in-the-loop
Jiwei Li, Alexander H Miller, Sumit Chopra, Marc’Aurelio Ranzato, and Jason Weston. 2016 · 2016
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Dialog-based language learning
Jason E Weston. 2016 · 2016
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Modular multitask reinforcement learning with policy sketches
Jacob Andreas, Dan Klein, and Sergey Levine. 2017 · 2017
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Gated-Attention Architectures for Task-Oriented Language Grounding
Devendra Singh Chaplot, Kanthashree Mysore Sathyendra, Rama Kumar Pasumarthi, Dheeraj Rajagopal, and Ruslan Salakhutdinov. 2017 · 2017
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Teaching machines to describe images with natural language feedback
Sanja Fidler et al. 2017 · 2017
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Beating Atari with Natural Language Guided Reinforcement Learning
Russell Kaplan, Christopher Sauer, and Alexander Sosa. 2017 · 2017
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TL;DR: Mining Reddit to learn automatic summarization
Michael Völske, Martin Potthast, Shahbaz Syed, and Benno Stein. 2017 · 2017
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e-snli: Natural language inference with natural language explanations
Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz, and Phil Blunsom. 2018 · 2018
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A Deep Reinforced Model for Abstractive Summarization
Romain Paulus, Caiming Xiong, and Richard Socher. 2018 · 2018
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Improving Language Understanding by Generative Pre-Training
Alec Radford and Karthik Narasimhan. 2018 · 2018
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Guide me: Interacting with deep networks
Christian Rupprecht, Iro Laina, Nassir Navab, Gregory D Hager, and Federico Tombari. 2018 · 2018
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Using Natural Language for Reward Shaping in Reinforcement Learning
Prasoon Goyal, Scott Niekum, and Raymond J. Mooney. 2019 · 2019
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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 · 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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Scaling language models: Methods, analysis & insights from training gopher
Jack W Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican, Jordan Hoffmann, Francis Song, John Aslanides, Sarah Henderson, Roman Ring, Susannah Young, et al. 2021 · 2021
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Multitask prompted training enables zero-shot task generalization
Victor Sanh, Albert Webson, Colin Raffel, Stephen H Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, et al. 2021 · 2021
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Supervising Model Attention with Human Explanations for Robust Natural Language Inference
Joe Stacey, Yonatan Belinkov, and Marek Rei. 2021 · 2021
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Learning rewards from linguistic feedback
Theodore R Sumers, Mark K Ho, Robert D Hawkins, Karthik Narasimhan, and Thomas L Griffiths. 2021 · 2021
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Measuring association between labels and free-text rationales
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Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020 · 2020
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Learning to summarize with human feedback
Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul F Christiano. 2020 · 2020
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Program synthesis with large language models
Jacob Austin, Augustus Odena, Maxwell I. Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie J. Cai, Michael Terry, Quoc V. Le, and Charles Sutton. 2021 · 2021
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Peter Hase and Mohit Bansal. 2021 · 2021
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TruthfulQA: Measuring How Models Mimic Human Falsehoods
Stephanie Lin, Jacob Hilton, and Owain Evans. 2021 · 2021
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Cut the carp: Fishing for zero-shot story evaluation
Shahbuland Matiana, JR Smith, Ryan Teehan, Louis Castricato, Stella Biderman, Leo Gao, and Spencer Frazier. 2021 · 2021
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WebGPT: Browser-assisted question-answering with human feedback
Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, et al. 2021 · 2021
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Sarah Wiegreffe, Ana Marasović, and Noah A. Smith. 2021 · 2021
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Can language models learn from explanations in context?
Andrew K Lampinen, Ishita Dasgupta, Stephanie CY Chan, Kory Matthewson, Michael Henry Tessler, Antonia Creswell, James L McClelland, Jane X Wang, and Felix Hill. 2022 · 2022
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Zero-Shot Reward Specification via Grounded Natural Language
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Text and Code Embeddings by Contrastive Pre-Training
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Training language models to follow instructions with human feedback
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Semantic exploration from language abstractions and pretrained representations
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Finetuned Language Models are Zero-Shot Learners
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Speak to your parser: Interactive text-to-SQL with natural language feedback
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