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Compositional generalization, representing the model's ability to generate text with new attribute combinations obtained by recombining single attributes from the training data, is a crucial property for multi-aspect controllable text generation (MCTG) methods.
Language models are few-shot learners
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Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy Hospedales. 2018 · 2018
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Plug and play language models: A simple approach to controlled text generation
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Towards controllable and personalized review generation
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Roberta: A robustly optimized bert pretraining approach
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Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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Measuring compositional generalization: A comprehensive method on realistic data
Daniel Keysers, Nathanael Schärli, Nathan Scales, Hylke Buisman, Daniel Furrer, Sergii Kashubin, Nikola Momchev, Danila Sinopalnikov, Lukasz Stafiniak, Tibor Tihon, Dmitry Tsarkov, Xiao Wang, Marc van Zee, and Olivier Bousquet. 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
Controllable natural language generation with contrastive prefixes
Jing Qian, Li Dong, Yelong Shen, Furu Wei, and Weizhu Chen. 2022b · 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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DisCup: Discriminator cooperative unlikelihood prompt-tuning for controllable text generation
Hanqing Zhang and Dawei Song. 2022 · 2022
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Disentangled sequence to sequence learning for compositional generalization
Hao Zheng and Mirella Lapata. 2022 · 2022
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Compositional generalization for multi-label text classification: A data-augmentation approach
Yuyang Chai, Zhuang Li, Jiahui Liu, Lei Chen, Fei Li, Donghong Ji, and Chong Teng. 2023 · 2023
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Meta-learning to compositionally generalize
Henry Conklin, Bailin Wang, Kenny Smith, and Ivan Titov. 2021 · 2021
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Span-based semantic parsing for compositional generalization
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Improving compositional generalization in classification tasks via structure annotations
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Gedi: Generative discriminator guided sequence generation
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Prefix-tuning: Optimizing continuous prompts for generation
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On compositional generalization of neural machine translation
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Compositional semantic parsing with large language models
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Controllable text generation via probability density estimation in the latent space
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An extensible plug-and-play method for multi-aspect controllable text generation
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Learning to substitute spans towards improving compositional generalization
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ChatGPT — openai.com
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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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Seen to unseen: Exploring compositional generalization of multi-attribute controllable dialogue generation
Weihao Zeng, Lulu Zhao, Keqing He, Ruotong Geng, Jingang Wang, Wei Wu, and Weiran Xu. 2023 · 2023
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Air-decoding: Attribute distribution reconstruction for decoding-time controllable text generation
Tianqi Zhong, Quan Wang, Jingxuan Han, Yongdong Zhang, and Zhendong Mao. 2023 · 2023
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Understanding and patching compositional reasoning in llms
Zhaoyi Li, Gangwei Jiang, Hong Xie, Linqi Song, Defu Lian, and Ying Wei. 2024 · 2024
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Ask one more time: Self-agreement improves reasoning of language models in (almost) all scenarios
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