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Large Language Models (LLMs) have showcased their In-Context Learning (ICL) capabilities, enabling few-shot learning without the need for gradient updates.
The PASCAL recognising textual entailment challenge
Ido Dagan, Oren Glickman, and Bernardo Magnini. 2005 · 2005
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Ng, and Christopher Potts. 2013 · 2013
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Second-order stochastic optimization for machine learning in linear time
Naman Agarwal, Brian Bullins, and Elad Hazan. 2017 · 2017
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang. 2017 · 2017
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Neural network acceptability judgments
Alex Warstadt, Amanpreet Singh, and Samuel R Bowman. 2018 · 2018
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Understanding the origins of bias in word embeddings
Marc-Etienne Brunet, Colleen Alkalay-Houlihan, Ashton Anderson, and Richard Zemel. 2019 · 2019
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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 · 2020
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton. 2020 · 2020
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Influence function based data poisoning attacks to top-n recommender systems
Minghong Fang, Neil Zhenqiang Gong, and Jia Liu. 2020 · 2020
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What neural networks memorize and why: Discovering the long tail via influence estimation
Vitaly Feldman and Chiyuan Zhang. 2020 · 2020
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Explaining black box predictions and unveiling data artifacts through influence functions
Xiaochuang Han, Byron C. Wallace, and Yulia Tsvetkov. 2020 · 2020
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Dense passage retrieval for open-domain question answering
Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick S. H. Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020 · 2020
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Learning augmentation network via influence functions
Donghoon Lee, Hyunsin Park, Trung Pham, and Chang D. Yoo. 2020 · 2020
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Influence functions in deep learning are fragile
Samyadeep Basu, Phil Pope, and Soheil Feizi. 2021 · 2021
Cited alongside, same era.
Making pre-trained language models better few-shot learners
Tianyu Gao, Adam Fisch, and Danqi Chen. 2021 · 2021
Cited alongside, same era.
Influence tuning: Demoting spurious correlations via instance attribution and instance-driven updates
Xiaochuang Han and Yulia Tsvetkov. 2021 · 2021
Cited alongside, same era.
Subpopulation data poisoning attacks
Matthew Jagielski, Giorgio Severi, Niklas Pousette Harger, and Alina Oprea. 2021 · 2021
Cited alongside, same era.
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2021 · 2021
Cited alongside, same era.
Data curation alone can stabilize in-context learning
Ting-Yun Chang and Robin Jia. 2023 · 2023
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A survey for in-context learning
Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Zhiyong Wu, Baobao Chang, Xu Sun, Jingjing Xu, Lei Li, and Zhifang Sui. 2023 · 2023
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Studying large language model generalization with influence functions
Roger B. Grosse, Juhan Bae, Cem Anil, Nelson Elhage, Alex Tamkin, Amirhossein Tajdini, Benoit Steiner, Dustin Li, Esin Durmus, Ethan Perez, Evan Hubinger, Kamile Lukosiute, Karina Nguyen, Nicholas Joseph, Sam McCandlish, Jared Kaplan, and Samuel R. Bowman. 2023 · 2023
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Unified demonstration retriever for in-context learning
Xiaonan Li, Kai Lv, Hang Yan, Tianyang Lin, Wei Zhu, Yuan Ni, Guotong Xie, Xiaoling Wang, and Xipeng Qiu. 2023 · 2023
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Finding support examples for in-context learning
Xiaonan Li and Xipeng Qiu. 2023 · 2023
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Sejoon Oh, Sungchul Kim, Ryan A. Rossi, and Srijan Kumar. 2021 · 2021
Cited alongside, same era.
Datamodels: Understanding predictions with data and data with predictions
Andrew Ilyas, Sung Min Park, Logan Engstrom, Guillaume Leclerc, and Aleksander Madry. 2022 · 2022
Cited alongside, same era.
Resolving training biases via influence-based data relabeling
Shuming Kong, Yanyan Shen, and Linpeng Huang. 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.
Learning to retrieve prompts for in-context learning
Ohad Rubin, Jonathan Herzig, and Jonathan Berant. 2022 · 2022
Cited alongside, same era.
An explanation of in-context learning as implicit bayesian inference
Sang Michael Xie, Aditi Raghunathan, Percy Liang, and Tengyu Ma. 2022 · 2022
Cited alongside, same era.
An empirical study of GPT-3 for few-shot knowledge-based VQA
Zhengyuan Yang, Zhe Gan, Jianfeng Wang, Xiaowei Hu, Yumao Lu, Zicheng Liu, and Lijuan Wang. 2022 · 2022
Cited alongside, same era.
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Dr.icl: Demonstration-retrieved in-context learning
Man Luo, Xin Xu, Zhuyun Dai, Panupong Pasupat, Seyed Mehran Kazemi, Chitta Baral, Vaiva Imbrasaite, and Vincent Y. Zhao. 2023 · 2023
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In-context example selection with influences
Tai Nguyen and Eric Wong. 2023 · 2023
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In-context learning with iterative demonstration selection
Chengwei Qin, Aston Zhang, Anirudh Dagar, and Wenming Ye. 2023 · 2023
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Reticl: Sequential retrieval of in-context examples with reinforcement learning
Alexander Scarlatos and Andrew S. Lan. 2023 · 2023
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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, Aurélien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023 · 2023
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Xinyi Wang, Wanrong Zhu, and William Yang Wang. 2023 · 2023
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Self-adaptive in-context learning: An information compression perspective for in-context example selection and ordering
Zhiyong Wu, Yaoxiang Wang, Jiacheng Ye, and Lingpeng Kong. 2023 · 2023
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