Fetching the paper…
Reading the bibliography…
Prompt-based learning has been an effective paradigm for large pretrained language models (LLM), enabling few-shot or even zero-shot learning.
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, et al. 2020 · 1901
Earlier work this paper cites.
Particle swarm optimization
James Kennedy and Russell Eberhart. 1995 · 1948
Earlier work this paper cites.
k-means++: the advantages of careful seeding
David Arthur and Sergei Vassilvitskii. 2007 · 2007
Earlier work this paper cites.
A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
Earlier work this paper cites.
Character-level convolutional networks for text classification
Xiang Zhang, Junbo Jake Zhao, and Yann LeCun. 2015 · 2015
Earlier work this paper cites.
Particle swarm optimization for single objective continuous space problems: a review
Mohammad Reza Bonyadi and Zbigniew Michalewicz. 2017 · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
Bayesian optimization of combinatorial structures
Ricardo Baptista and Matthias Poloczek. 2018 · 2018
Earlier work this paper cites.
XNLI: Evaluating cross-lingual sentence representations
Alexis Conneau, Ruty Rinott, Guillaume Lample, Adina Williams, Samuel Bowman, Holger Schwenk, and Veselin Stoyanov. 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 Bowman. 2018 · 2018
Earlier work this paper cites.
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
Earlier work this paper cites.
Neural architecture generator optimization
Robin Ru, Pedro Esperanca, and Fabio Maria Carlucci. 2020 · 2020
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 · 2020
Earlier work this paper cites.
Word-level textual adversarial attacking as combinatorial optimization
Yuan Zang, Fanchao Qi, Chenghao Yang, Zhiyuan Liu, Meng Zhang, Qun Liu, and Maosong Sun. 2020 · 2020
Cited alongside, same era.
Parameter-efficient multi-task fine-tuning for transformers via shared hypernetworks
Rabeeh Karimi Mahabadi, Sebastian Ruder, Mostafa Dehghani, and James Henderson. 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
Xiang Lisa Li and Percy Liang. 2021 · 2021
Cited alongside, same era.
True few-shot learning with language models
Ethan Perez, Douwe Kiela, and Kyunghyun Cho. 2021 · 2021
Cited alongside, same era.
Think global and act local: Bayesian optimisation over high-dimensional categorical and mixed search spaces
Z-icl: Zero-shot in-context learning with pseudo-demonstrations
Xinxi Lyu, Sewon Min, Iz Beltagy, Luke Zettlemoyer, and Hannaneh Hajishirzi. 2022 · 2022
Later among the works it cites.
Learning to retrieve prompts for in-context learning
Ohad Rubin, Jonathan Herzig, and Jonathan Berant. 2022 · 2022
Later among the works it cites.
BBTv2: Towards a gradient-free future with large language models
Tianxiang Sun, Zhengfu He, Hong Qian, Yunhua Zhou, Xuanjing Huang, and Xipeng Qiu. 2022a · 2022
Later among the works it cites.
On redundancy and diversity in cell-based neural architecture search
Xingchen Wan, Binxin Ru, Pedro M Esperança, and Zhenguo Li. 2022 · 2022
Later among the works it cites.
Self-icl: Zero-shot in-context learning with self-generated demonstrations
Wei-Lin Chen, Cheng-Kuang Wu, and Hsin-Hsi Chen. 2023 · 2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Xingchen Wan, Vu Nguyen, Huong Ha, Bin Xin Ru, Cong Lu, and Michael A. Osborne. 2021 · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Bayesian optimization over discrete and mixed spaces via probabilistic reparameterization
Sam Daulton, Xingchen Wan, David Eriksson, Maximilian Balandat, Michael A Osborne, and Eytan Bakshy. 2022 · 2022
Cited alongside, same era.
RLPrompt: Optimizing discrete text prompts with reinforcement learning
Mingkai Deng, Jianyu Wang, Cheng-Ping Hsieh, Yihan Wang, Han Guo, Tianmin Shu, Meng Song, Eric Xing, and Zhiting Hu. 2022 · 2022
Cited alongside, same era.
Towards a unified view of parameter-efficient transfer learning
Junxian He, Chunting Zhou, Xuezhe Ma, Taylor Berg-Kirkpatrick, and Graham Neubig. 2022 · 2022
Cited alongside, same era.
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
Cited alongside, same era.
Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022
Cited alongside, same era.
Black-box prompt learning for pre-trained language models
Shizhe Diao, Zhichao Huang, Ruijia Xu, Xuechun Li, LIN Yong, Xiao Zhou, and Tong Zhang. 2023 · 2023
Closest in time.
Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2023 · 2023
Closest in time.
OpenAI. 2023 · 2023
Closest in time.
GrIPS: Gradient-free, edit-based instruction search for prompting large language models
Archiki Prasad, Peter Hase, Xiang Zhou, and Mohit Bansal. 2023 · 2023
Closest in time.
Better zero-shot reasoning with self-adaptive prompting
Xingchen Wan, Ruoxi Sun, Hanjun Dai, Sercan Arik, and Tomas Pfister. 2023a · 2023
Closest in time.
One network, many masks: Towards more parameter-efficient transfer learning
Guangtao Zeng, Peiyuan Zhang, and Wei Lu. 2023 · 2023
Closest in time.
TEMPERA: Test-time prompt editing via reinforcement learning
Tianjun Zhang, Xuezhi Wang, Denny Zhou, Dale Schuurmans, and Joseph E. Gonzalez. 2023 · 2023
Closest in time.