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Energy-based models (EBMs) have gained popularity for controlled text generation due to their high applicability to a wide range of constraints.
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
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Ctrl: A conditional transformer language model for controllable generation
Nitish Shirish Keskar, Bryan McCann, Lav R Varshney, Caiming Xiong, and Richard Socher. 2019 · 1909
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A tutorial on energy-based learning
Yann LeCun, Sumit Chopra, Raia Hadsell, M Ranzato, and Fujie Huang. 2006 · 2006
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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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Bayesian learning via stochastic gradient langevin dynamics
Max Welling and Yee Whye Teh. 2011 · 2011
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Estimating or propagating gradients through stochastic neurons for conditional computation
Yoshua Bengio, Nicholas Léonard, and Aaron Courville. 2013 · 2013
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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Jake Zhao, and Yann LeCun. 2015 · 2015
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Improved image captioning via policy gradient optimization of spider
Siqi Liu, Zhenhai Zhu, Ning Ye, Sergio Guadarrama, and Kevin Murphy. 2017 · 2017
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Self-critical sequence training for image captioning
Steven J. Rennie, Etienne Marcheret, Youssef Mroueh, Jerret Ross, and Vaibhava Goel. 2017 · 2017
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Differentiable lower bound for expected BLEU score
Vlad Zhukov and Maksim Kretov. 2017 · 2017
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A differentiable BLEU loss. analysis and first results
Noe Casas, José A. R. Fonollosa, and Marta R. Costa-jussà. 2018 · 2018
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Controlled text generation for data augmentation in intelligent artificial agents
Nikolaos Malandrakis, Minmin Shen, Anuj Kumar Goyal, Shuyang Gao, Abhishek Sethi, and Angeliki Metallinou. 2019 · 2019
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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
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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 · 2020
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Data augmentation using pre-trained transformer models
Varun Kumar, Ashutosh Choudhary, and Eunah Cho. 2020 · 2020
Controlled text generation as continuous optimization with multiple constraints
Sachin Kumar, Eric Malmi, Aliaksei Severyn, and Yulia Tsvetkov. 2021 · 2021
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FUDGE: controlled text generation with future discriminators
Kevin Yang and Dan Klein. 2021 · 2021
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Exposing the implicit energy networks behind masked language models via metropolis–hastings
Kartik Goyal, Chris Dyer, and Taylor Berg-Kirkpatrick. 2022 · 2022
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Jigsaw: Large language models meet program synthesis
Naman Jain, Skanda Vaidyanath, Arun Shankar Iyer, Nagarajan Natarajan, Suresh Parthasarathy, Sriram K. Rajamani, and Rahul Sharma. 2022 · 2022
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Gradient-based constrained sampling from language models
Sachin Kumar, Biswajit Paria, and Yulia Tsvetkov. 2022 · 2022
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Cited alongside, same era.
CommonGen: A constrained text generation challenge for generative commonsense reasoning
Bill Yuchen Lin, Wangchunshu Zhou, Ming Shen, Pei Zhou, Chandra Bhagavatula, Yejin Choi, and Xiang Ren. 2020 · 2020
Cited alongside, same era.
Data boost: Text data augmentation through reinforcement learning guided conditional generation
Ruibo Liu, Guangxuan Xu, Chenyan Jia, Weicheng Ma, Lili Wang, and Soroush Vosoughi. 2020 · 2020
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POINTER: Constrained progressive text generation via insertion-based generative pre-training
Yizhe Zhang, Guoyin Wang, Chunyuan Li, Zhe Gan, Chris Brockett, and Bill Dolan. 2020 · 2020
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A distributional approach to controlled text generation
Muhammad Khalifa, Hady Elsahar, and Marc Dymetman. 2021 · 2021
Cited alongside, same era.
Gedi: Generative discriminator guided sequence generation
Ben Krause, Akhilesh Deepak Gotmare, Bryan McCann, Nitish Shirish Keskar, Shafiq R. Joty, Richard Socher, and Nazneen Fatema Rajani. 2021 · 2021
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Mix and match: Learning-free controllable text generationusing energy language models
Fatemehsadat Mireshghallah, Kartik Goyal, and Taylor Berg-Kirkpatrick. 2022 · 2022
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Cold decoding: Energy-based constrained text generation with langevin dynamics
Lianhui Qin, Sean Welleck, Daniel Khashabi, and Yejin Choi. 2022 · 2022
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Tailor: A prompt-based approach to attribute-based controlled text generation
Kexin Yang, Dayiheng Liu, Wenqiang Lei, Baosong Yang, Mingfeng Xue, Boxing Chen, and Jun Xie. 2022 · 2022
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Discup: Discriminator cooperative unlikelihood prompt-tuning for controllable text generation
Hanqing Zhang and Dawei Song. 2022 · 2022
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Don’t take it literally: An edit-invariant sequence loss for text generation
Guangyi Liu, Zichao Yang, Tianhua Tao, Xiaodan Liang, Junwei Bao, Zhen Li, Xiaodong He, Shuguang Cui, and Zhiting Hu. 2022 · 2078
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