Fetching the paper…
Reading the bibliography…
Multi-aspect controllable text generation is a more challenging and practical task than single-aspect control.
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, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020a · 1901
Earlier work this paper cites.
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. 2020b · 1901
Earlier work this paper cites.
CTRL - A Conditional Transformer Language Model for Controllable Generation
Nitish Shirish Keskar, Bryan McCann, Lav 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 Christiano, and Geoffrey Irving. 2019 · 1909
Earlier work this paper cites.
A tutorial on energy-based learning
Yann LeCun, Sumit Chopra, Raia Hadsell, M Ranzato, and F Huang. 2006 · 2006
Earlier work this paper cites.
Cross-domain sentiment classification via spectral feature alignment
Sinno Jialin Pan, Xiaochuan Ni, Jian-Tao Sun, Qiang Yang, and Zheng Chen. 2010 · 2010
Earlier work this paper cites.
Learning word vectors for sentiment analysis
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. 2011 · 2011
Earlier work this paper cites.
Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun. 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.
Controlling linguistic style aspects in neural language generation
Jessica Ficler and Yoav Goldberg. 2017 · 2017
Earlier work this paper cites.
Toward controlled generation of text
Zhiting Hu, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, and Eric P. Xing. 2017 · 2017
Earlier work this paper cites.
Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 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.
Pre-train and plug-in: Flexible conditional text generation with variational auto-encoders
Yu Duan, Canwen Xu, Jiaxin Pei, Jialong Han, and Chenliang Li. 2020 · 2020
Cited alongside, same era.
A distributional approach to controlled text generation
Muhammad Khalifa, Hady Elsahar, and Marc Dymetman. 2020 · 2020
Cited alongside, same era.
Plug and play autoencoders for conditional text generation
Florian Mai, Nikolaos Pappas, Ivan Montero, Noah A. Smith, and James Henderson. 2020 · 2020
Cited alongside, same era.
GeDi: Generative discriminator guided sequence generation
Attribute alignment: Controlling text generation from pre-trained language models
Dian Yu, Zhou Yu, and Kenji Sagae. 2021 · 2021
Later among the works it cites.
Fine-grained controllable text generation using non-residual prompting
Fredrik Carlsson, Joey Öhman, Fangyu Liu, Severine Verlinden, Joakim Nivre, and Magnus Sahlgren. 2022 · 2022
Closest in time.
Improving controllable text generation with position-aware weighted decoding
Yuxuan Gu, Xiaocheng Feng, Sicheng Ma, Jiaming Wu, Heng Gong, and Bing Qin. 2022 · 2022
Closest in time.
Constrained sampling from language models via langevin dynamics in embedding spaces
Sachin Kumar, Biswajit Paria, and Yulia Tsvetkov. 2022 · 2022
Closest in time.
Mix and match: Learning-free controllable text generationusing energy language models
Fatemehsadat Mireshghallah, Kartik Goyal, and Taylor Berg-Kirkpatrick. 2022 · 2022
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Ben Krause, Akhilesh Deepak Gotmare, Bryan McCann, Nitish Shirish Keskar, Shafiq Joty, Richard Socher, and Nazneen Fatema Rajani. 2021 · 2021
Cited alongside, same era.
Controlled text generation as continuous optimization with multiple constraints
Sachin Kumar, Eric Malmi, Aliaksei Severyn, and Yulia Tsvetkov. 2021 · 2021
Cited alongside, same era.
Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang. 2021 · 2021
Cited alongside, same era.
Plug-and-blend: A framework for controllable story generation with blended control codes
Zhiyu Lin and Mark Riedl. 2021 · 2021
Cited alongside, same era.
Mauve: Measuring the gap between neural text and human text using divergence frontiers
Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, John Thickstun, Sean Welleck, Yejin Choi, and Zaid Harchaoui. 2021 · 2021
Cited alongside, same era.
FUDGE: Controlled text generation with future discriminators
Kevin Yang and Dan Klein. 2021 · 2021
Cited alongside, same era.
Pengcheng He, Jianfeng Gao, and Weizhu Chen. 2021a
Cited in the paper.
Controllable natural language generation with contrastive prefixes
Jing Qian, Li Dong, Yelong Shen, Furu Wei, and Weizhu Chen. 2022 · 2022
Closest in time.
Cold decoding: Energy-based constrained text generation with langevin dynamics
Lianhui Qin, Sean Welleck, Daniel Khashabi, and Yejin Choi. 2022 · 2022
Closest in time.
Generative visual prompt: Unifying distributional control of pre-trained generative models
Chen Henry Wu, Saman Motamed, Shaunak Srivastava, and Fernando De la Torre. 2022 · 2022
Closest in time.
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
Closest in time.
Opt: Open pre-trained transformer language models
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, et al. 2022 · 2022
Closest in time.