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Prompt-based pre-trained language models (PLMs) paradigm have succeeded substantially in few-shot natural language processing (NLP) tasks.
Language models are few-shot learners
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The curious case of neural text degeneration
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Roberta: A robustly optimized bert pretraining approach
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Plug and play language models: A simple approach to controlled text generation
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Practical issues in temporal difference learning
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Policy gradient methods for reinforcement learning with function approximation
Sutton, R. S.; McAllester, D.; Singh, S.; and Mansour, Y. 1999 · 1999
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Exploiting cloze questions for few shot text classification and natural language inference
Schick, T.; and Schütze, H. 2020a · 2001
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Electra: Pre-training text encoders as discriminators rather than generators
Clark, K.; Luong, M.-T.; Le, Q. V.; and Manning, C. D. 2020 · 2003
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Mining and summarizing customer reviews
Hu, M.; and Liu, B. 2004 · 2004
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Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
Pang, B.; and Lee, L. 2005 · 2005
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It’s not just size that matters: Small language models are also few-shot learners
Schick, T.; and Schütze, H. 2020b · 2009
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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 · 2010
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Making pre-trained language models better few-shot learners
Gao, T.; Fisch, A.; and Chen, D. 2020 · 2012
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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. Y.; and Potts, C. 2013 · 2013
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Character-level convolutional networks for text classification
Zhang, X.; Zhao, J.; and LeCun, Y. 2015 · 2015
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Improving language understanding by generative pre-training
Radford, A.; Narasimhan, K.; Salimans, T.; Sutskever, I.; et al. 2018 · 2018
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
Wang, A.; Singh, A.; Michael, J.; Hill, F.; Levy, O.; and Bowman, S. R. 2018 · 2018
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Commonsense knowledge mining from pretrained models
Davison, J.; Feldman, J.; and Rush, A. M. 2019 · 2019
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Unified language model pre-training for natural language understanding and generation
Dong, L.; Yang, N.; Wang, W.; Wei, F.; Liu, X.; Wang, Y.; Gao, J.; Zhou, M.; and Hon, H.-W. 2019 · 2019
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Bartscore: Evaluating generated text as text generation
Yuan, W.; Neubig, G.; and Liu, P. 2021 · 2021
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Input-tuning: Adapting unfamiliar inputs to frozen pretrained models
An, S.; Li, Y.; Lin, Z.; Liu, Q.; Chen, B.; Fu, Q.; Chen, W.; Zheng, N.; and Lou, J.-G. 2022 · 2022
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Promptsource: An integrated development environment and repository for natural language prompts
Bach, S. H.; Sanh, V.; Yong, Z.-X.; Webson, A.; Raffel, C.; Nayak, N. V.; Sharma, A.; Kim, T.; Bari, M. S.; Fevry, T.; et al. 2022 · 2022
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Rlprompt: Optimizing discrete text prompts with reinforcement learning
Deng, M.; Wang, J.; Hsieh, C.-P.; Wang, Y.; Guo, H.; Shu, T.; Song, M.; Xing, E. P.; and Hu, Z. 2022 · 2022
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Rethinking the role of demonstrations: What makes in-context learning work?
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Jiang, Z.; Xu, F. F.; Araki, J.; and Neubig, G. 2020 · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C.; Shazeer, N.; Roberts, A.; Lee, K.; Narang, S.; Matena, M.; Zhou, Y.; Li, W.; and Liu, P. J. 2020 · 2020
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BERTese: Learning to speak to BERT
Haviv, A.; Berant, J.; and Globerson, A. 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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Prefix-tuning: Optimizing continuous prompts for generation
Li, X. L.; and Liang, P. 2021 · 2021
Cited alongside, same era.
Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity
Lu, Y.; Bartolo, M.; Moore, A.; Riedel, S.; and Stenetorp, P. 2021 · 2021
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Cross-task generalization via natural language crowdsourcing instructions
Mishra, S.; Khashabi, D.; Baral, C.; and Hajishirzi, H. 2021 · 2021
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Min, S.; Lyu, X.; Holtzman, A.; Artetxe, M.; Lewis, M.; Hajishirzi, H.; and Zettlemoyer, L. 2022 · 2022
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Grips: Gradient-free, edit-based instruction search for prompting large language models
Prasad, A.; Hase, P.; Zhou, X.; and Bansal, M. 2022 · 2022
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Controllable natural language generation with contrastive prefixes
Qian, J.; Dong, L.; Shen, Y.; Wei, F.; and Chen, W. 2022 · 2022
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Black-box tuning for language-model-as-a-service
Sun, T.; Shao, Y.; Qian, H.; Huang, X.; and Qiu, X. 2022 · 2022
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Large language models are human-level prompt engineers
Zhou, Y.; Muresanu, A. I.; Han, Z.; Paster, K.; Pitis, S.; Chan, H.; and Ba, J. 2022 · 2022
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Anil, R.; Dai, A. M.; Firat, O.; Johnson, M.; Lepikhin, D.; Passos, A.; Shakeri, S.; Taropa, E.; Bailey, P.; Chen, Z.; et al. 2023 · 2023
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Chataug: Leveraging chatgpt for text data augmentation
Dai, H.; Liu, Z.; Liao, W.; Huang, X.; Wu, Z.; Zhao, L.; Liu, W.; Liu, N.; Li, S.; Zhu, D.; et al. 2023 · 2023
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Llama: Open and efficient foundation language models
Touvron, H.; Lavril, T.; Izacard, G.; Martinet, X.; Lachaux, M.-A.; Lacroix, T.; Rozière, B.; Goyal, N.; Hambro, E.; Azhar, F.; et al. 2023 · 2023
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ZeroShotDataAug: Generating and Augmenting Training Data with ChatGPT
Ubani, S.; Polat, S. O.; and Nielsen, R. 2023 · 2023
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