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Large language models generate fluent texts and can follow natural language instructions to solve a wide range of tasks without task-specific training.
Language models are few-shot learners
Tom 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 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. 2020 · 1901
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RoBERTa: A robustly optimized BERT pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
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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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Harvesting paragraph-level question-answer pairs from Wikipedia
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Constituency parsing with a self-attentive encoder
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Fast lexically constrained decoding with dynamic beam allocation for neural machine translation
Matt Post and David Vilar. 2018 · 2018
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Dear sir or madam, may I introduce the GYAFC dataset: Corpus, benchmarks and metrics for formality style transfer
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NeuroLogic decoding: (Un)supervised neural text generation with predicate logic constraints
Ximing Lu, Peter West, Rowan Zellers, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. 2021 · 2021
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Pre-training text-to-text transformers for concept-centric common sense
Wangchunshu Zhou, Dong-Ho Lee, Ravi Kiran Selvam, Seyeon Lee, and Xiang Ren. 2021 · 2021
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Deep learning for text style transfer: A survey
Di Jin, Zhijing Jin, Zhiting Hu, Olga Vechtomova, and Rada Mihalcea. 2022 · 2022
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Gradient-based constrained sampling from language models
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Diffusion-LM improves controllable text generation
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NeuroLogic A ∗ esque decoding: Constrained text generation with lookahead heuristics
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Jingjing Xu, Xu Sun, Qi Zeng, Xiaodong Zhang, Xuancheng Ren, Houfeng Wang, and Wenjie Li. 2018 · 2018
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Training neural machine translation to apply terminology constraints
Georgiana Dinu, Prashant Mathur, Marcello Federico, and Yaser Al-Onaizan. 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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BERT-based lexical substitution
Wangchunshu Zhou, Tao Ge, Ke Xu, Furu Wei, and Ming Zhou. 2019 · 2019
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The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2020 · 2020
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CommonGen: A constrained text generation challenge for generative commonsense reasoning
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Politeness transfer: A tag and generate approach
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Ximing Lu, Sean Welleck, Peter West, Liwei Jiang, Jungo Kasai, Daniel Khashabi, Ronan Le Bras, Lianhui Qin, Youngjae Yu, Rowan Zellers, Noah A. Smith, and Yejin Choi. 2022 · 2022
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Multitask prompted training enables zero-shot task generalization
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Finetuned language models are zero-shot learners
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OPT: Open pre-trained transformer language models
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Locally Typical Sampling
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Don’t take it literally: An edit-invariant sequence loss for text generation
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