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In recent years, there has been a growing interest in the development of language models capable of generating text with controllable attributes.
CTRL: A conditional transformer language model for controllable generation
Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, Caiming Xiong, and Richard Socher. 2019 · 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 F. Christiano, and Geoffrey Irving. 2019 · 1909
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Gamma correction in constant luminance color television systems
Sidney Applebaum. 1952 · 1952
Earlier work this paper cites.
Applications and explanations of zipf’s law
David M. W. Powers. 1998 · 1998
Earlier work this paper cites.
Nonlinear characterization of a simple process in human vision
Peter Neri. 2009 · 2009
Earlier work this paper cites.
Directed beam search: Plug-and-play lexically constrained language generation
Damian Pascual, Beni Egressy, Florian Bolli, and Roger Wattenhofer. 2020 · 2012
Earlier work this paper cites.
Oriol Vinyals and Quoc V. Le. 2015 · 2015
Earlier work this paper cites.
A diversity-promoting objective function for neural conversation models
Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and Bill Dolan. 2016 · 2016
Earlier work this paper cites.
Hafez: an interactive poetry generation system
Marjan Ghazvininejad, Xing Shi, Jay Priyadarshi, and Kevin Knight. 2017 · 2017
Earlier work this paper cites.
Affect-lm: A neural language model for customizable affective text generation
Sayan Ghosh, Mathieu Chollet, Eugene Laksana, Louis-Philippe Morency, and Stefan Scherer. 2017 · 2017
Earlier work this paper cites.
Hierarchical neural story generation
Angela Fan, Mike Lewis, and Yann N. Dauphin. 2018 · 2018
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Realtoxicityprompts: Evaluating neural toxic degeneration in language models
Samuel Gehman, Suchin Gururangan, Maarten Sap, Yejin Choi, and Noah A. Smith. 2020 · 2020
Cited alongside, same era.
The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2020 · 2020
Cited alongside, same era.
Powertransformer: Unsupervised controllable revision for biased language correction
Neurologic a*esque decoding: Constrained text generation with lookahead heuristics
Ximing Lu, Sean Welleck, Peter West, Liwei Jiang, Jungo Kasai, Daniel Khashabi, Ronan Le Bras, Lianhui Qin, Youngjae Yu, Rowan Zellers, Noah A. Smith, and Yejin Choi. 2021 · 2021
Later among the works it cites.
A plug-and-play method for controlled text generation
Damian Pascual, Beni Egressy, Clara Meister, Ryan Cotterell, and Roger Wattenhofer. 2021 · 2021
Later among the works it cites.
Controllable neural text generation
Lilian Weng. 2021 · 2021
Later among the works it cites.
Raise a child in large language model: Towards effective and generalizable fine-tuning
Runxin Xu, Fuli Luo, Zhiyuan Zhang, Chuanqi Tan, Baobao Chang, Songfang Huang, and Fei Huang. 2021 · 2021
Later among the works it cites.
FUDGE: controlled text generation with future discriminators
Kevin Yang and Dan Klein. 2021 · 2021
Later among the works it cites.
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Xinyao Ma, Maarten Sap, Hannah Rashkin, and Yejin Choi. 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.
Investigating softmax tempering for training neural machine translation models
Raj Dabre and Atsushi Fujita. 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
Cited alongside, same era.
Dexperts: Decoding-time controlled text generation with experts and anti-experts
Alisa Liu, Maarten Sap, Ximing Lu, Swabha Swayamdipta, Chandra Bhagavatula, Noah A. Smith, and Yejin Choi. 2021 · 2021
Cited alongside, same era.
Xu Zou, Da Yin, Qingyang Zhong, Hongxia Yang, Zhilin Yang, and Jie Tang. 2021 · 2021
Later among the works it cites.
Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022
Closest in time.
Controllable text generation with neurally-decomposed oracle
Tao Meng, Sidi Lu, Nanyun Peng, and Kai-Wei Chang. 2022 · 2022
Closest in time.
Mix and match: Learning-free controllable text generation using energy language models
Fatemehsadat Mireshghallah, Kartik Goyal, and Taylor Berg-Kirkpatrick. 2022 · 2022
Closest in time.