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Large language models (LLMs) effectively generate fluent text when the target output follows natural language patterns.
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. 2020 · 1901
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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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Cg-bert: Conditional text generation with bert for generalized few-shot intent detection
Congying Xia, Chenwei Zhang, Hoang Nguyen, Jiawei Zhang, and Philip S. Yu. 2020 · 2004
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Automatically constructing a corpus of sentential paraphrases
William B. Dolan and Chris Brockett. 2005 · 2005
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Compositional generalization in semantic parsing: Pre-training vs. specialized architectures
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Active learning literature survey
Burr Settles. 2009 · 2009
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Otto Fabius, Joost R. van Amersfoort, and Diederik P. Kingma. 2015 · 2015
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Yushi Wang, Jonathan Berant, and Percy Liang. 2015 · 2015
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A diversity-promoting objective function for neural conversation models
Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and Bill Dolan. 2016 · 2016
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Sequence to backward and forward sequences: A content-introducing approach to generative short-text conversation
Lili Mou, Yiping Song, Rui Yan, Ge Li, Lu Zhang, and Zhi Jin. 2016 · 2016
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Neural machine translation of rare words with subword units
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Lexically constrained decoding for sequence generation using grid beam search
Chris Hokamp and Qun Liu. 2017 · 2017
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Zhiting Hu, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, and Eric P. Xing. 2017 · 2017
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Sigmoid-weighted linear units for neural network function approximation in reinforcement learning
Stefan Elfwing, Eiji Uchibe, and Kenji Doya. 2018 · 2018
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Contextual augmentation: Data augmentation by words with paradigmatic relations
Sosuke Kobayashi. 2018 · 2018
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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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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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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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Topic-guided variational auto-encoder for text generation
Wenlin Wang, Zhe Gan, Hongteng Xu, Ruiyi Zhang, Guoyin Wang, Dinghan Shen, Changyou Chen, and Lawrence Carin. 2019 · 2019
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EDA: Easy data augmentation techniques for boosting performance on text classification tasks
Jason Wei and Kai Zou. 2019 · 2019
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Lili Yao, Nanyun Peng, Ralph M. Weischedel, Kevin Knight, Dongyan Zhao, and Rui Yan. 2019 · 2019
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PAWS: Paraphrase Adversaries from Word Scrambling
Yuan Zhang, Jason Baldridge, and Luheng He. 2019 · 2019
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Do not have enough data? deep learning to the rescue!
Ateret Anaby-Tavor, Boaz Carmeli, Esther Goldbraich, Amir Kantor, George Kour, Segev Shlomov, Naama Tepper, and Naama Zwerdling. 2020 · 2020
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Low-resource domain adaptation for compositional task-oriented semantic parsing
Xilun Chen, Asish Ghoshal, Yashar Mehdad, Luke Zettlemoyer, and Sonal Gupta. 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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Data augmentation using pre-trained transformer models
Varun Kumar, Ashutosh Choudhary, and Eunah Cho. 2020 · 2020
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BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
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SSMBA: Self-supervised manifold based data augmentation for improving out-of-domain robustness
Nathan Ng, Kyunghyun Cho, and Marzyeh Ghassemi. 2020 · 2020
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ATTEMPT: parameter-efficient multi-task tuning via attentional mixtures of soft prompts
Akari Asai, Mohammadreza Salehi, Matthew E. Peters, and Hannaneh Hajishirzi. 2022 · 2022
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Data augmentation for intent classification
Derek Chen and Claire Yin. 2022 · 2022
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Weakly supervised data augmentation through prompting for dialogue understanding
Maximillian Chen, Alexandros Papangelis, Chenyang Tao, Andy Rosenbaum, Seokhwan Kim, Yang Liu, Zhou Yu, and Dilek Hakkani-Tur. 2022 · 2022
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Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Y. Zhao, Yanping Huang, Andrew M. Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei. 2022 · 2022
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Exploring the limits of transfer learning with a unified text-to-text transformer
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GOLD: Improving out-of-scope detection in dialogues using data augmentation
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Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang. 2021 · 2021
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A distributional lens for multi-aspect controllable text generation
Yuxuan Gu, Xiaocheng Feng, Sicheng Ma, Lingyuan Zhang, Heng Gong, and Bing Qin. 2022 · 2022
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LoRA: Low-rank adaptation of large language models
Edward J Hu, yelong shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2022 · 2022
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Rethinking the role of demonstrations: What makes in-context learning work?
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Learning to compose soft prompts for compositional zero-shot learning
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Godel: Large-scale pre-training for goal-directed dialog
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Controllable natural language generation with contrastive prefixes
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Controllable natural language generation with contrastive prefixes
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Evaluating the impact of model scale for compositional generalization in semantic parsing
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Multitask prompted training enables zero-shot task generalization
Victor Sanh, Albert Webson, Colin Raffel, Stephen Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Arun Raja, Manan Dey, M Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal V. Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang, Matteo Manica, Sheng Shen, Zheng Xin Yong, Harshit Pandey, Rachel Bawden, Thomas Wang, Trishala Neeraj, Jos Rozen, Abheesht Sharma, Andrea Santilli, Thibault Févry, Jason Alan Fries, Ryan Teehan, Teven Le Scao, Stella Biderman, Leo Gao, Thomas Wolf, and Alexander M. Rush. 2022 · 2022
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The power of prompt tuning for low-resource semantic parsing
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SPoT: Better frozen model adaptation through soft prompt transfer
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PromDA: Prompt-based data augmentation for low-resource NLU tasks
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Chain-of-thought prompting elicits reasoning in large language models
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An explanation of in-context learning as implicit bayesian inference
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Tailor: A prompt-based approach to attribute-based controlled text generation
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Cross-domain named entity recognition via graph matching
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Faith and fate: Limits of transformers on compositionality
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