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Controllable text generation (CTG) aims to generate text with desired attributes, and decoding-time-based methods have shown promising performance on this task.
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, et al. 2020 · 1901
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Roberta: A robustly optimized bert pretraining approach
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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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Fine-tuning language models from human preferences
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Cocon: A self-supervised approach for controlled text generation
Alvin Chan, Yew-Soon Ong, Bill Pung, Aston Zhang, and Jie Fu. 2020 · 2006
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Learning word vectors for sentiment analysis
Andrew Maas, Raymond E Daly, Peter T Pham, Dan Huang, Andrew Y Ng, and Christopher Potts. 2011 · 2011
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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015 · 2015
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A diversity-promoting objective function for neural conversation models
Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and William B Dolan. 2016 · 2016
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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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. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
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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 · 2020
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Plug-and-play conversational models
Andrea Madotto, Etsuko Ishii, Zhaojiang Lin, Sumanth Dathathri, and Pascale Fung. 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 William B Dolan. 2020 · 2020
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Gedi: Generative discriminator guided sequence generation
Ben Krause, Akhilesh Deepak Gotmare, Bryan McCann, Nitish Shirish Keskar, Shafiq Joty, Richard Socher, and Nazneen Fatema Rajani. 2021 · 2021
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Controlled text generation as continuous optimization with multiple constraints
Sachin Kumar, Eric Malmi, Aliaksei Severyn, and Yulia Tsvetkov. 2021 · 2021
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The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Fine-grained controllable text generation using non-residual prompting
Fredrik Carlsson, Joey Öhman, Fangyu Liu, Severine Verlinden, Joakim Nivre, and Magnus Sahlgren. 2022 · 2022
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Improving controllable text generation with position-aware weighted decoding
Yuxuan Gu, Xiaocheng Feng, Sicheng Ma, Jiaming Wu, Heng Gong, and Bing Qin. 2022a · 2022
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Ctrleval: An unsupervised reference-free metric for evaluating controlled text generation
Pei Ke, Hao Zhou, Yankai Lin, Peng Li, Jie Zhou, Xiaoyan Zhu, and Minlie Huang. 2022 · 2022
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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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Controllable natural language generation with contrastive prefixes
Jing Qian, Li Dong, Yelong Shen, Furu Wei, and Weizhu Chen. 2022 · 2022
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Xiang Lisa Li and Percy Liang. 2021 · 2021
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Plug-and-blend: a framework for plug-and-play controllable story generation with sketches
Zhiyu Lin and Mark O Riedl. 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.
Mention flags (mf): Constraining transformer-based text generators
Yufei Wang, Ian Wood, Stephen Wan, Mark Dras, and Mark Johnson. 2021 · 2021
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Fudge: Controlled text generation with future discriminators
Kevin Yang and Dan Klein. 2021 · 2021
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Attribute alignment: Controlling text generation from pre-trained language models
Dian Yu, Zhou Yu, and Kenji Sagae. 2021 · 2021
Cited alongside, same era.
A distributional lens for multi-aspect controllable text generation
Yuxuan Gu, Xiaocheng Feng, Sicheng Ma, Lingyuan Zhang, Heng Gong, and Bing Qin. 2022b
Cited in the paper.
A contrastive framework for neural text generation
Yixuan Su, Tian Lan, Yan Wang, Dani Yogatama, Lingpeng Kong, and Nigel Collier. 2022 · 2022
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Perplexity from plm is unreliable for evaluating text quality
Yequan Wang, Jiawen Deng, Aixin Sun, and Xuying Meng. 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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A survey of controllable text generation using transformer-based pre-trained language models
Hanqing Zhang, Haolin Song, Shaoyu Li, Ming Zhou, and Dawei Song. 2022 · 2022
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Zerogen: Zero-shot multimodal controllable text generation with multiple oracles
Haoqin Tu, Bowen Yang, and Xianfeng Zhao. 2023 · 2023
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