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Prompt-based models have gathered a lot of attention from researchers due to their remarkable advancements in the fields of zero-shot and few-shot learning.
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
Earlier work this paper cites.
Building a question answering test collection
Ellen M Voorhees and Dawn M Tice. 2000 · 2000
Earlier work this paper cites.
Exploiting cloze questions for few shot text classification and natural language inference
Timo Schick and Hinrich Schütze. 2020 · 2001
Earlier work this paper cites.
Mining and summarizing customer reviews
Minqing Hu and Bing Liu. 2004 · 2004
Earlier work this paper cites.
Language models are few-shot learners
Tom B. 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 M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher 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 · 2005
Earlier work this paper cites.
It’s not just size that matters: Small language models are also few-shot learners
Timo Schick and Hinrich Schütze. 2020 · 2009
Earlier work this paper cites.
Autoprompt: Eliciting knowledge from language models with automatically generated prompts
Taylor Shin, Yasaman Razeghi, Robert L Logan IV, Eric Wallace, and Sameer Singh. 2020 · 2010
Earlier work this paper cites.
Making pre-trained language models better few-shot learners
Tianyu Gao, Adam Fisch, and Danqi Chen. 2020 · 2012
Earlier work this paper cites.
Fixing weight decay regularization in adam
Ilya Loshchilov and Frank Hutter. 2017 · 2017
Earlier work this paper cites.
Get to the point: Summarization with pointer-generator networks
Abigail See, Peter J. Liu, and Christopher D. Manning. 2017 · 2017
Earlier work this paper cites.
Adafactor: Adaptive learning rates with sublinear memory cost
Noam Shazeer and Mitchell Stern. 2018 · 2018
Earlier work this paper cites.
Glue: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman. 2018 · 2018
Earlier work this paper cites.
Superglue: A stickier benchmark for general-purpose language understanding systems
Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. 2019 · 2019
Earlier work this paper cites.
PADA: A prompt-based autoregressive approach for adaptation to unseen domains
Eyal Ben-David, Nadav Oved, and Roi Reichart. 2021 · 2021
Cited alongside, same era.
Openprompt: An open-source framework for prompt-learning
Ning Ding, Shengding Hu, Weilin Zhao, Yulin Chen, Zhiyuan Liu, Hai-Tao Zheng, and Maosong Sun. 2021 · 2021
Cited alongside, same era.
A framework for few-shot language model evaluation
Leo Gao, Jonathan Tow, Stella Biderman, Sid Black, Anthony DiPofi, Charles Foster, Laurence Golding, Jeffrey Hsu, Kyle McDonell, Niklas Muennighoff, Jason Phang, Laria Reynolds, Eric Tang, Anish Thite, Ben Wang, Kevin Wang, and Andy Zou. 2021 · 2021
Cited alongside, same era.
The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
Cited alongside, same era.
Prefix-tuning: Optimizing continuous prompts for generation
Black-box tuning for language-model-as-a-service
Tianxiang Sun, Yunfan Shao, Hong Qian, Xuanjing Huang, and Xipeng Qiu. 2022 · 2022
Later among the works it cites.
Challenging big-bench tasks and whether chain-of-thought can solve them
Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc V. Le, Ed H. Chi, Denny Zhou, and Jason Wei. 2022 · 2022
Later among the works it cites.
Idpg: An instance-dependent prompt generation method
Zhuofeng Wu, Sinong Wang, Jiatao Gu, Rui Hou, Yuxiao Dong, VG Vydiswaran, and Hao Ma. 2022 · 2022
Later among the works it cites.
Automatic chain of thought prompting in large language models
Zhuosheng Zhang, Aston Zhang, Mu Li, and Alex Smola. 2022 · 2022
Later among the works it cites.
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Xiang Lisa Li and Percy Liang. 2021 · 2021
Cited alongside, same era.
Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity
Yao Lu, Max Bartolo, Alastair Moore, Sebastian Riedel, and Pontus Stenetorp. 2021 · 2021
Cited alongside, same era.
Calibrate before use: Improving few-shot performance of language models
Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh. 2021 · 2021
Cited alongside, same era.
Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Alex Castro-Ros, Marie Pellat, Kevin Robinson, Dasha Valter, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei. 2022 · 2022
Cited alongside, same era.
Ppt: Pre-trained prompt tuning for few-shot learning
Yuxian Gu, Xu Han, Zhiyuan Liu, and Minlie Huang. 2022 · 2022
Cited alongside, same era.
Late prompt tuning: A late prompt could be better than many prompts
Xiangyang Liu, Tianxiang Sun, Xuanjing Huang, and Xipeng Qiu. 2022 · 2022
Cited alongside, same era.
The biases of pre-trained language models: An empirical study on prompt-based sentiment analysis and emotion detection
Rui Mao, Qian Liu, Kai He, Wei Li, and Erik Cambria. 2022 · 2022
Cited alongside, same era.
Grips: Gradient-free, edit-based instruction search for prompting large language models
Archiki Prasad, Peter Hase, Xiang Zhou, and Mohit Bansal. 2022 · 2022
Cited alongside, same era.
Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, and Jimmy Ba. 2022 · 2022
Later among the works it cites.
Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
BIG bench authors. 2023 · 2023
Closest in time.
Meal: Stable and active learning for few-shot prompting
Abdullatif Köksal, Timo Schick, and Hinrich Schütze. 2023 · 2023
Closest in time.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2023 · 2023
Closest in time.
Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023 · 2023
Closest in time.
How far can camels go? exploring the state of instruction tuning on open resources
Yizhong Wang, Hamish Ivison, Pradeep Dasigi, Jack Hessel, Tushar Khot, Khyathi Raghavi Chandu, David Wadden, Kelsey MacMillan, Noah A. Smith, Iz Beltagy, and Hannaneh Hajishirzi. 2023 · 2023
Closest in time.
Dynamic prompting: A unified framework for prompt tuning
Xianjun Yang, Wei Cheng, Xujiang Zhao, Linda Petzold, and Haifeng Chen. 2023 · 2023
Closest in time.
Tree of thoughts: Deliberate problem solving with large language models
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L Griffiths, Yuan Cao, and Karthik Narasimhan. 2023 · 2023
Closest in time.