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Large language models (LLMs) have exhibited striking in-context learning (ICL) ability to adapt to target tasks with a few input-output demonstrations.
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
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Note on the sampling error of the difference between correlated proportions or percentages
Quinn McNemar. 1947 · 1947
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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
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Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych. 2019 · 2019
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Prompt programming for large language models: Beyond the few-shot paradigm
Laria Reynolds and Kyle McDonell. 2021 · 2021
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An explanation of in-context learning as implicit bayesian inference
Sang Michael Xie, Aditi Raghunathan, Percy Liang, and Tengyu Ma. 2021 · 2021
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What learning algorithm is in-context learning? investigations with linear models
Ekin Akyürek, Dale Schuurmans, Jacob Andreas, Tengyu Ma, and Denny Zhou. 2022 · 2022
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Data distributional properties drive emergent in-context learning in transformers
Stephanie Chan, Adam Santoro, Andrew Lampinen, Jane Wang, Aaditya Singh, Pierre Richemond, James McClelland, and Felix Hill. 2022 · 2022
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Palm: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al. 2022 · 2022
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Why can gpt learn in-context? language models secretly perform gradient descent as meta optimizers
Damai Dai, Yutao Sun, Li Dong, Yaru Hao, Zhifang Sui, and Furu Wei. 2022 · 2022
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Complexity-based prompting for multi-step reasoning
Yao Fu, Hao Peng, Ashish Sabharwal, Peter Clark, and Tushar Khot. 2022 · 2022
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Demystifying prompts in language models via perplexity estimation
Hila Gonen, Srini Iyer, Terra Blevins, Noah A Smith, and Luke Zettlemoyer. 2022 · 2022
Earlier work this paper cites.
Hyuhng Joon Kim, Hyunsoo Cho, Junyeob Kim, Taeuk Kim, Kang Min Yoo, and Sang-goo Lee. 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.
Diverse demonstrations improve in-context compositional generalization
Itay Levy, Ben Bogin, and Jonathan Berant. 2022 · 2022
Cited alongside, same era.
What makes good in-context examples for GPT-3?
Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, and Weizhu Chen. 2022 · 2022
Cited alongside, same era.
Z-icl: Zero-shot in-context learning with pseudo-demonstrations
Selective annotation makes language models better few-shot learners
Hongjin Su, Jungo Kasai, Chen Henry Wu, Weijia Shi, Tianlu Wang, Jiayi Xin, Rui Zhang, Mari Ostendorf, Luke Zettlemoyer, Noah A Smith, et al. 2022 · 2022
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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, et al. 2022 · 2022
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Transformers learn in-context by gradient descent
Johannes von Oswald, Eyvind Niklasson, Ettore Randazzo, João Sacramento, Alexander Mordvintsev, Andrey Zhmoginov, and Max Vladymyrov. 2022 · 2022
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Do prompt-based models really understand the meaning of their prompts?
Albert Webson and Ellie Pavlick. 2022 · 2022
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Xinxi Lyu, Sewon Min, Iz Beltagy, Luke Zettlemoyer, and Hannaneh Hajishirzi. 2022 · 2022
Cited alongside, same era.
Text and patterns: For effective chain of thought, it takes two to tango
Aman Madaan and Amir Yazdanbakhsh. 2022 · 2022
Cited alongside, same era.
Rethinking the role of demonstrations: What makes in-context learning work?
Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2022b · 2022
Cited alongside, same era.
Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022 · 2022
Cited alongside, same era.
Impact of pretraining term frequencies on few-shot reasoning
Yasaman Razeghi, Robert L Logan IV, Matt Gardner, and Sameer Singh. 2022 · 2022
Cited alongside, same era.
Learning to retrieve prompts for in-context learning
Ohad Rubin, Jonathan Herzig, and Jonathan Berant. 2022 · 2022
Cited alongside, same era.
An information-theoretic approach to prompt engineering without ground truth labels
Taylor Sorensen, Joshua Robinson, Christopher Rytting, Alexander Shaw, Kyle Rogers, Alexia Delorey, Mahmoud Khalil, Nancy Fulda, and David Wingate. 2022 · 2022
Cited alongside, same era.
Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, et al. 2022 · 2022
Cited alongside, same era.
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, et al. 2022 · 2022
Later among the works it cites.
Self-adaptive in-context learning
Zhiyong Wu, Yaoxiang Wang, Jiacheng Ye, and Lingpeng Kong. 2022 · 2022
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Active example selection for in-context learning
Yiming Zhang, Shi Feng, and Chenhao Tan. 2022a · 2022
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Large language models are human-level prompt engineers
Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, and Jimmy Ba. 2022 · 2022
Later among the works it cites.
Rohan Anil, Andrew M Dai, Orhan Firat, Melvin Johnson, Dmitry Lepikhin, Alexandre Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Zhifeng Chen, et al. 2023 · 2023
Closest in time.
Miles Turpin, Julian Michael, Ethan Perez, and Samuel R Bowman. 2023 · 2023
Closest in time.
Larger language models do in-context learning differently
Jerry Wei, Jason Wei, Yi Tay, Dustin Tran, Albert Webson, Yifeng Lu, Xinyun Chen, Hanxiao Liu, Da Huang, Denny Zhou, et al. 2023 · 2023
Closest in time.