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We propose a Distributional Approach for addressing Controlled Text Generation from pre-trained Language Models (LMs).
Way off-policy batch deep reinforcement learning of implicit human preferences in dialog
Natasha Jaques, Asma Ghandeharioun, Judy Hanwen Shen, Craig Ferguson, Àgata Lapedriza, Noah Jones, Shixiang Gu, and Rosalind W. Picard · 1907
Earlier work this paper cites.
CTRL: A conditional transformer language model for controllable generation
Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, Caiming Xiong, and Richard Socher · 1909
Earlier work this paper cites.
Fine-tuning language models from human preferences
Daniel M. Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B. Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving · 1909
Earlier work this paper cites.
Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, and Jamie Brew · 1910
Earlier work this paper cites.
Distributional Reinforcement Learning For Energy-Based Sequential Models
Tetiana Parshakova, Jean-Marc Andreoli, and Marc Dymetman · 1912
Earlier work this paper cites.
Information theory and statistical mechanics
E. T. Jaynes · 1957
Earlier work this paper cites.
I-Divergence Geometry of Probability Distributions and Minimization Problems
I. Csiszar · 1975
Earlier work this paper cites.
Maxent, mathematics, and information theory
I. Csiszár · 1996
Earlier work this paper cites.
Methods of Information Geometry
Sun-ichi Amari and Hiroshi Nagaoka · 2000
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Whole-sentence exponential language models: A vehicle for linguistic-statistical integration
Ronald Rosenfeld, Stanley F. Chen, and Xiaojin Zhu · 2001
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Training products of experts by minimizing contrastive divergence
Geoffrey E. Hinton · 2002
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Generalized accept-reject sampling schemes
George Casella, Christian P Robert, Martin T Wells, et al · 2004
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Information theory and statistics: A tutorial
Imre Csiszár and Paul C. Shields · 2004
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Stereoset: Measuring stereotypical bias in pretrained language models
Moin Nadeem, Anna Bethke, and Siva Reddy · 2004
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Language models are few-shot learners
T. Brown, B. Mann, Nick Ryder, Melanie Subbiah, J. Kaplan, P. Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, G. Krüger, Tom Henighan, R. Child, Aditya Ramesh, D. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, E. Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, J. Clark, Christopher Berner, Sam McCandlish, A. Radford, Ilya Sutskever, and Dario Amodei · 2005
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 · 2005
Earlier work this paper cites.
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Christian P. Robert and George Casella · 2005
Earlier work this paper cites.
Towards controllable biases in language generation
Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, and Nanyun Peng · 2005
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A Tutorial on Energy-Based Learning
Yann LeCun, Sumit Chopra, Raia Hadsell, Marc’Aurelio Ranzato, and Fu Jie Huang · 2006
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A unified energy-based framework for unsupervised learning
Marc’Aurelio Ranzato, Y-Lan Boureau, Sumit Chopra, and Yann LeCun · 2007
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Importance Sampling
Art B. Owen · 2013
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First women, second sex: Gender bias in wikipedia
Eduardo Graells-Garrido, Mounia Lalmas, and Filippo Menczer · 2015
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Globally Normalized Transition-Based Neural Networks
Daniel Andor, Chris Alberti, David Weiss, Aliaksei Severyn, Alessandro Presta, Kuzman Ganchev, Slav Petrov, and Michael Collins · 2016
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Structured prediction energy networks
David Belanger and Andrew McCallum · 2016
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Deep directed generative models with energy-based probability estimation
Taesup Kim and Yoshua Bengio · 2016
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GANS for sequences of discrete elements with the gumbel-softmax distribution
Matt J. Kusner and José Miguel Hernández-Lobato · 2016
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Texygen: A benchmarking platform for text generation models
Yaoming Zhu, Sidi Lu, Lei Zheng, Jiaxian Guo, Weinan Zhang, Jun Wang, and Yong Yu · 2018
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Identifying and reducing gender bias in word-level language models
Shikha Bordia and Samuel R. Bowman · 2019
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Meansum: A neural model for unsupervised multi-document abstractive summarization
Eric Chu and Peter J. Liu · 2019
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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 Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
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Neural text generation from structured data with application to the biography domain
Rémi Lebret, David Grangier, and Michael Auli · 2016
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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
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Deep reinforcement learning for dialogue generation
Jiwei Li, Will Monroe, Alan Ritter, Dan Jurafsky, Michel Galley, and Jianfeng Gao · 2016
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How NOT to evaluate your dialogue system: An empirical study of unsupervised evaluation metrics for dialogue response generation
Chia-Wei Liu, Ryan Lowe, Iulian Serban, Michael Noseworthy, Laurent Charlin, and Joelle Pineau · 2016
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Sequence level training with recurrent neural networks
Marc’Aurelio Ranzato, Sumit Chopra, Michael Auli, and Wojciech Zaremba · 2016
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Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Lukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, and Jeffrey Dean · 2016
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An actor-critic algorithm for sequence prediction
Dzmitry Bahdanau, Philemon Brakel, Kelvin Xu, Anirudh Goyal, Ryan Lowe, Joelle Pineau, Aaron C. Courville, and Yoshua Bengio · 2017
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What makes a good conversation? how controllable attributes affect human judgments
Abigail See, Stephen Roller, Douwe Kiela, and Jason Weston · 2019
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The woman worked as a babysitter: On biases in language generation
Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, and Nanyun Peng · 2019
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The woman worked as a babysitter: On biases in language generation
Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, and Nanyun Peng · 2019
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Evaluating gender bias in machine translation
Gabriel Stanovsky, Noah A. Smith, and Luke Zettlemoyer · 2019
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Controllable neural story plot generation via reward shaping
Pradyumna Tambwekar, Murtaza Dhuliawala, Lara J. Martin, Animesh Mehta, Brent Harrison, and Mark O. Riedl · 2019
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Universal adversarial triggers for attacking and analyzing NLP
Eric Wallace, Shi Feng, Nikhil Kandpal, Matt Gardner, and Sameer Singh · 2019
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A. Bakhtin, Y. Deng, S. Gross, Myle Ott, Marc’Aurelio Ranzato, and Arthur Szlam · 2020
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Language (technology) is power: A critical survey of “bias” in NLP
Su Lin Blodgett, Solon Barocas, Hal Daumé III, and Hanna Wallach · 2020
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Language gans falling short
Massimo Caccia, Lucas Caccia, William Fedus, Hugo Larochelle, Joelle Pineau, and Laurent Charlin · 2020
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Plug and play language models: A simple approach to controlled text generation
Sumanth Dathathri, Andrea Madotto, Janice Lan, Jane Hung, Eric Frank, Piero Molino, Jason Yosinski, and Rosanne Liu · 2020
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Residual energy-based models for text generation
Yuntian Deng, Anton Bakhtin, Myle Ott, Arthur Szlam, and Marc’Aurelio Ranzato · 2020
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The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi · 2020
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Assessing gender bias in machine translation: a case study with google translate
Marcelo O. R. Prates, Pedro H. C. Avelar, and Luís C. Lamb · 2020
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Engine: Energy-based inference networks for non-autoregressive machine translation
Lifu Tu, Richard Yuanzhe Pang, Sam Wiseman, and Kevin Gimpel · 2020
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On the dangers of stochastic parrots: Can language models be too big?
Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell · 2021
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