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Transformer-based Large Language Models (LLMs) have shown exceptional language generation capabilities in response to text-based prompts.
The Curious Case of Neural Text Degeneration
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Educating Text Autoencoders: Latent Representation Guidance via Denoising
Shen, T.; Mueller, J.; Barzilay, R.; and Jaakkola, T. 2020 · 1905
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CTRL: A Conditional Transformer Language Model for Controllable Generation
Keskar, N. S.; McCann, B.; Varshney, L. R.; Xiong, C.; and Socher, R. 2019 · 1909
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Fine-Tuning Language Models from Human Preferences
Ziegler, D. M.; Stiennon, N.; Wu, J.; Brown, T. B.; Radford, A.; Amodei, D.; Christiano, P.; and Irving, G. 2020 · 1909
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Trainable Greedy Decoding for Neural Machine Translation
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Long short-term memory
Hochreiter, S.; and Schmidhuber, J. 1997 · 1997
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Language Models are Few-Shot Learners
Brown, T. B.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; Agarwal, S.; Herbert-Voss, A.; Krueger, G.; Henighan, T.; Child, R.; Ramesh, A.; Ziegler, D. M.; Wu, J.; Winter, C.; Hesse, C.; Chen, M.; Sigler, E.; Litwin, M.; Gray, S.; Chess, B.; Clark, J.; Berner, C.; McCandlish, S.; Radford, A.; Sutskever, I.; and Amodei, D. 2020 · 2005
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CoCon: A Self-Supervised Approach for Controlled Text Generation
Chan, A.; Ong, Y.-S.; Pung, B.; Zhang, A.; and Fu, J. 2022 · 2006
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Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
Socher, R.; Perelygin, A.; Wu, J.; Chuang, J.; Manning, C. D.; Ng, A.; and Potts, C. 2013 · 2013
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Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
Cho, K.; van Merriënboer, B.; Gulcehre, C.; Bahdanau, D.; Bougares, F.; Schwenk, H.; and Bengio, Y. 2014 · 2014
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Neural Machine Translation by Jointly Learning to Align and Translate
Bahdanau, D.; Cho, K.; and Bengio, Y. 2016 · 2016
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A Diversity-Promoting Objective Function for Neural Conversation Models
Li, J.; Galley, M.; Brockett, C.; Gao, J.; and Dolan, B. 2016 · 2016
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Learning to Translate in Real-time with Neural Machine Translation
Gu, J.; Neubig, G.; Cho, K.; and Li, V. O. 2017 · 2017
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Toward Controlled Generation of Text
Hu, Z.; Yang, Z.; Liang, X.; Salakhutdinov, R.; and Xing, E. P. 2017 · 2017
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Style Transfer from Non-Parallel Text by Cross-Alignment
Shen, T.; Lei, T.; Barzilay, R.; and Jaakkola, T. 2017 · 2017
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Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, L.; and Polosukhin, I. 2017 · 2017
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SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
Yu, L.; Zhang, W.; Wang, J.; and Yu, Y. 2017 · 2017
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A Stable and Effective Learning Strategy for Trainable Greedy Decoding
Chen, Y.; Li, V. O.; Cho, K.; and Bowman, S. 2018 · 2018
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Hierarchical Neural Story Generation
Fan, A.; Lewis, M.; and Dauphin, Y. 2018 · 2018
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AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts
Shin, T.; Razeghi, Y.; Logan IV, R. L.; Wallace, E.; and Singh, S. 2020 · 2020
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WARP: Word-level Adversarial ReProgramming
Hambardzumyan, K.; Khachatrian, H.; and May, J. 2021 · 2021
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LoRA: Low-Rank Adaptation of Large Language Models
Hu, E. J.; Shen, Y.; Wallis, P.; Allen-Zhu, Z.; Li, Y.; Wang, S.; Wang, L.; and Chen, W. 2021 · 2021
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GeDi: Generative Discriminator Guided Sequence Generation
Krause, B.; Gotmare, A. D.; McCann, B.; Keskar, N. S.; Joty, S.; Socher, R.; and Rajani, N. F. 2021 · 2021
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The Power of Scale for Parameter-Efficient Prompt Tuning
Lester, B.; Al-Rfou, R.; and Constant, N. 2021 · 2021
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Learning to Write with Cooperative Discriminators
Holtzman, A.; Buys, J.; Forbes, M.; Bosselut, A.; Golub, D.; and Choi, Y. 2018 · 2018
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Dear Sir or Madam, May I Introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer
Rao, S.; and Tetreault, J. 2018 · 2018
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Adversarial Reprogramming of Neural Networks
Elsayed, G. F.; Goodfellow, I.; and Sohl-Dickstein, J. 2019 · 2019
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Parameter-Efficient Transfer Learning for NLP
Houlsby, N.; Giurgiu, A.; Jastrzebski, S.; Morrone, B.; De Laroussilhe, Q.; Gesmundo, A.; Attariyan, M.; and Gelly, S. 2019 · 2019
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Decoupled Weight Decay Regularization
Loshchilov, I.; and Hutter, F. 2019 · 2019
Cited alongside, same era.
Language Models are Unsupervised Multitask Learners
Radford, A.; Wu, J.; Child, R.; Luan, D.; Amodei, D.; and Sutskever, I. 2019 · 2019
Cited alongside, same era.
Plug and Play Language Models: A Simple Approach to Controlled Text Generation
Dathathri, S.; Madotto, A.; Lan, J.; Hung, J.; Frank, E.; Molino, P.; Yosinski, J.; and Liu, R. 2020 · 2020
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Li, X. L.; and Liang, P. 2021 · 2021
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A Plug-and-Play Method for Controlled Text Generation
Pascual, D.; Egressy, B.; Meister, C.; Cotterell, R.; and Wattenhofer, R. 2021 · 2021
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It’s Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners
Schick, T.; and Schütze, H. 2021b · 2021
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FUDGE: Controlled Text Generation With Future Discriminators
Yang, K.; and Klein, D. 2021 · 2021
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Novelty Controlled Paraphrase Generation with Retrieval Augmented Conditional Prompt Tuning
Chowdhury, J. R.; Zhuang, Y.; and Wang, S. 2022 · 2022
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Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution
Kumar, A.; Raghunathan, A.; Jones, R.; Ma, T.; and Liang, P. 2022 · 2022
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Toxic Comment Classification Challenge
Jigsaw. 2017 · 2023
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OpenAI. 2023 · 2023
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