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As the text generation capabilities of large language models become increasingly prominent, recent studies have focused on controlling particular aspects of the generated text to make it more personalized.
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.
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Earlier work this paper cites.
The two-sample trimmed t for unequal population variances
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Ziqian Zeng, Yichun Yin, Yangqiu Song, and Ming Zhang. 2017 · 2017
Earlier work this paper cites.
Lexi: A tool for adaptive, personalized text simplification
Joachim Bingel, Gustavo Paetzold, and Anders Søgaard. 2018 · 2018
Earlier work this paper cites.
Personalizing lexical simplification
John Lee and Chak Yan Yeung. 2018 · 2018
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Content preserving text generation with attribute controls
Lajanugen Logeswaran, Honglak Lee, and Samy Bengio. 2018 · 2018
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Training millions of personalized dialogue agents
Pierre-Emmanuel Mazaré, Samuel Humeau, Martin Raison, and Antoine Bordes. 2018 · 2018
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Personalizing dialogue agents: I have a dog, do you have pets too?
Saizheng Zhang, Emily Dinan, Jack Urbanek, Arthur Szlam, Douwe Kiela, and Jason Weston. 2018 · 2018
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Parameter-efficient transfer learning for NLP
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. 2019 · 2019
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Multiple-attribute text rewriting
Guillaume Lample, Sandeep Subramanian, Eric Smith, Ludovic Denoyer, Marc’Aurelio Ranzato, and Y-Lan Boureau. 2019 · 2019
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Personalizing dialogue agents via meta-learning
Andrea Madotto, Zhaojiang Lin, Chien-Sheng Wu, and Pascale Fung. 2019 · 2019
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Personalizing grammatical error correction: Adaptation to proficiency level and L1
Maria Nadejde and Joel Tetreault. 2019 · 2019
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Justifying recommendations using distantly-labeled reviews and fine-grained aspects
One chatbot per person: Creating personalized chatbots based on implicit user profiles
Zhengyi Ma, Zhicheng Dou, Yutao Zhu, Hanxun Zhong, and Ji-Rong Wen. 2021 · 2021
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Personalized extractive summarization using an ising machine towards real-time generation of efficient and coherent dialogue scenarios
Hiroaki Takatsu, Takahiro Kashikawa, Koichi Kimura, Ryota Ando, and Yoichi Matsuyama. 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
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UserAdapter: Few-shot user learning in sentiment analysis
Wanjun Zhong, Duyu Tang, Jiahai Wang, Jian Yin, and Nan Duan. 2021 · 2021
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UserIdentifier: Implicit user representations for simple and effective personalized sentiment analysis
Fatemehsadat Mireshghallah, Vaishnavi Shrivastava, Milad Shokouhi, Taylor Berg-Kirkpatrick, Robert Sim, and Dimitrios Dimitriadis. 2022 · 2022
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Jianmo Ni, Jiacheng Li, and Julian McAuley. 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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The pile: An 800gb dataset of diverse text for language modeling
Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, et al. 2020 · 2020
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Evaluating approaches to personalizing language models
Milton King and Paul Cook. 2020 · 2020
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Control, generate, augment: A scalable framework for multi-attribute text generation
Giuseppe Russo, Nora Hollenstein, Claudiu Cristian Musat, and Ce Zhang. 2020 · 2020
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Examination and extension of strategies for improving personalized language modeling via interpolation
Liqun Shao, Sahitya Mantravadi, Tom Manzini, Alejandro Buendia, Manon Knoertzer, Soundar Srinivasan, and Chris Quirk. 2020 · 2020
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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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Language as a fingerprint: Self-supervised learning of user encodings using transformers
Roberta Rocca and Tal Yarkoni. 2022 · 2022
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Less is more: Learning to refine dialogue history for personalized dialogue generation
Hanxun Zhong, Zhicheng Dou, Yutao Zhu, Hongjin Qian, and Ji-Rong Wen. 2022 · 2022
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Pythia: A suite for analyzing large language models across training and scaling
Stella Biderman, Hailey Schoelkopf, Quentin Anthony, Herbie Bradley, Kyle O’Brien, Eric Hallahan, Mohammad Aflah Khan, Shivanshu Purohit, USVSN Sai Prashanth, Edward Raff, Aviya Skowron, Lintang Sutawika, and Oskar van der Wal. 2023 · 2023
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Mixture of soft prompts for controllable data generation
Derek Chen, Celine Lee, Yunan Lu, Domenic Rosati, and Zhou Yu. 2023 · 2023
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A confederacy of models: a comprehensive evaluation of LLMs on creative writing
Carlos Gómez-Rodríguez and Paul Williams. 2023 · 2023
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Zhiqiang Hu, Roy Ka-Wei Lee, and Nancy F. Chen. 2023 · 2023
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Challenges and applications of large language models
Jean Kaddour, Joshua Harris, Maximilian Mozes, Herbie Bradley, Roberta Raileanu, and Robert McHardy. 2023 · 2023
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Daisuke Oba, Naoki Yoshinaga, and Masashi Toyoda. 2023 · 2023
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Vipul Raheja, Dhruv Kumar, Ryan Koo, and Dongyeop Kang. 2023 · 2023
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Lamp: When large language models meet personalization
Alireza Salemi, Sheshera Mysore, Michael Bendersky, and Hamed Zamani. 2023 · 2023
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Tailor: A soft-prompt-based approach to attribute-based controlled text generation
Kexin Yang, Dayiheng Liu, Wenqiang Lei, Baosong Yang, Mingfeng Xue, Boxing Chen, and Jun Xie. 2023 · 2023
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