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Large Language Models (LLMs) can adapt to new tasks via in-context learning (ICL).
Some methods for classification and analysis of multivariate observations
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Toward semantics-based answer pinpointing
Eduard Hovy, Laurie Gerber, Ulf Hermjakob, Chin-Yew Lin, and Deepak Ravichandran · 2001
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Approximation algorithms , volume 1
Vijay V Vazirani · 2001
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Unsupervised construction of large paraphrase corpora: Exploiting massively parallel news sources
Bill Dolan, Chris Quirk, and Chris Brockett · 2004
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A note on the budgeted maximization of submodular functions
Andreas Krause and Carlos Guestrin · 2005
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The fifth pascal recognizing textual entailment challenge
Luisa Bentivogli, Peter Clark, Ido Dagan, and Danilo Giampiccolo · 2009
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Active learning for statistical phrase-based machine translation
Gholamreza Haffari, Maxim Roy, and Anoop Sarkar · 2009
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Active learning literature survey
Burr Settles · 2009
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Hidden factors and hidden topics: Understanding rating dimensions with review text
Julian McAuley and Jure Leskovec · 2013
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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
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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun · 2015
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
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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
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Don’t give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization
Shashi Narayan, Shay B. Cohen, and Mirella Lapata · 2018
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Active learning for convolutional neural networks: A core-set approach
Ozan Sener and Silvio Savarese · 2018
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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Practical, efficient, and customizable active learning for named entity recognition in the digital humanities
Alexander Erdmann, David Joseph Wrisley, Benjamin Allen, Christopher Brown, Sophie Cohen-Bodénès, Micha Elsner, Yukun Feng, Brian Joseph, Béatrice Joyeux-Prunel, and Marie-Catherine de Marneffe · 2019
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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
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Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych · 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, et al · 2020
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Active Learning for BERT: An Empirical Study
Liat Ein-Dor, Alon Halfon, Ariel Gera, Eyal Shnarch, Lena Dankin, Leshem Choshen, Marina Danilevsky, Ranit Aharonov, Yoav Katz, and Noam Slonim · 2020
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A survey of active learning for text classification using deep neural networks, 2020
Christopher Schröder and Andreas Niekler · 2020
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Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush · 2020
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On the opportunities and risks of foundation models
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al · 2021
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Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al · 2021
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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
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Towards understanding chain-of-thought prompting: An empirical study of what matters
Boshi Wang, Sewon Min, Xiang Deng, Jiaming Shen, You Wu, Luke Zettlemoyer, and Huan Sun · 2022
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Ground-truth labels matter: A deeper look into input-label demonstrations
Kang Min Yoo, Junyeob Kim, Hyuhng Joon Kim, Hyunsoo Cho, Hwiyeol Jo, Sang-Woo Lee, Sang-goo Lee, and Taeuk Kim · 2022
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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 · 2023
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Transformers as statisticians: Provable in-context learning with in-context algorithm selection
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Tianyu Gao, Adam Fisch, and Danqi Chen · 2021
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How can we know when language models know? on the calibration of language models for question answering
Zhengbao Jiang, Jun Araki, Haibo Ding, and Graham Neubig · 2021
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Datasets: A community library for natural language processing
Quentin Lhoest, Albert Villanova del Moral, Yacine Jernite, Abhishek Thakur, Patrick von Platen, Suraj Patil, Julien Chaumond, Mariama Drame, Julien Plu, Lewis Tunstall, Joe Davison, Mario Šaško, Gunjan Chhablani, Bhavitvya Malik, Simon Brandeis, Teven Le Scao, Victor Sanh, Canwen Xu, Nicolas Patry, Angelina McMillan-Major, Philipp Schmid, Sylvain Gugger, Clément Delangue, Théo Matussière, Lysandre Debut, Stas Bekman, Pierric Cistac, Thibault Goehringer, Victor Mustar, François Lagunas, Alexander Rush, and Thomas Wolf · 2021
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What makes good in-context examples for gpt- 3 3 ?
Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, and Weizhu Chen · 2021
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Active learning by acquiring contrastive examples
Katerina Margatina, Giorgos Vernikos, Loïc Barrault, and Nikolaos Aletras · 2021
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True few-shot learning with language models
Ethan Perez, Douwe Kiela, and Kyunghyun Cho · 2021
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GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model
Ben Wang and Aran Komatsuzaki · 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
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Yu Bai, Fan Chen, Huan Wang, Caiming Xiong, and Song Mei · 2023
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Rethinking the role of scale for in-context learning: An interpretability-based case study at 66 billion scale
Hritik Bansal, Karthik Gopalakrishnan, Saket Dingliwal, Sravan Bodapati, Katrin Kirchhoff, and Dan Roth · 2023
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Active prompting with chain-of-thought for large language models
Shizhe Diao, Pengcheng Wang, Yong Lin, and Tong Zhang · 2023
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A theory of emergent in-context learning as implicit structure induction
Michael Hahn and Navin Goyal · 2023
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In-context learning of large language models explained as kernel regression
Chi Han, Ziqi Wang, Han Zhao, and Heng Ji · 2023
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Calibrated simplex-mapping classification
Raoul Heese, Jochen Schmid, Michał Walczak, and Michael Bortz · 2023
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Finding supporting examples for in-context learning
Xiaonan Li and Xipeng Qiu · 2023
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Fairness-guided few-shot prompting for large language models
Huan Ma, Changqing Zhang, Yatao Bian, Lemao Liu, Zhirui Zhang, Peilin Zhao, Shu Zhang, Huazhu Fu, Qinghua Hu, and Bingzhe Wu · 2023
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Active learning principles for in-context learning with large language models, 2023
Katerina Margatina, Timo Schick, Nikolaos Aletras, and Jane Dwivedi-Yu · 2023
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Introducing mpt-7b: A new standard for open-source, commercially usable llms, 2023
MosaicML · 2023
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In-context example selection with influences
Tai Nguyen and Eric Wong · 2023
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Guilherme Penedo, Quentin Malartic, Daniel Hesslow, Ruxandra Cojocaru, Alessandro Cappelli, Hamza Alobeidli, Baptiste Pannier, Ebtesam Almazrouei, and Julien Launay · 2023
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Boosted prompt ensembles for large language models
Silviu Pitis, Michael R Zhang, Andrew Wang, and Jimmy Ba · 2023
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Automatic prompt augmentation and selection with chain-of-thought from labeled data
KaShun Shum, Shizhe Diao, and Tong Zhang · 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, et al · 2023
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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 · 2023
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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
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$k$NN prompting: Beyond-context learning with calibration-free nearest neighbor inference
Benfeng Xu, Quan Wang, Zhendong Mao, Yajuan Lyu, Qiaoqiao She, and Yongdong Zhang · 2023
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