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Prompting language models (LMs) is the main interface for applying them to new tasks.
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 · 1901
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Classification and Regression Trees
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Statistical decision-tree models for parsing
David M. Magerman. 1995 · 1995
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Bagging predictors
Leo Breiman. 1996 · 1996
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Experiments with a new boosting algorithm
Yoav Freund, Robert E Schapire, et al. 1996 · 1996
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Three generative, lexicalised models for statistical parsing
Michael Collins. 1997 · 1997
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A decision-theoretic generalization of on-line learning and an application to boosting
Yoav Freund and Robert E Schapire. 1997 · 1997
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Random forests
Leo Breiman. 2001 · 2001
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Toward semantics-based answer pinpointing
Eduard Hovy, Laurie Gerber, Ulf Hermjakob, Chin-Yew Lin, and Deepak Ravichandran. 2001 · 2001
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Learning question classifiers
Xin Li and Dan Roth. 2002 · 2002
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Machine learning in automated text categorization
Fabrizio Sebastiani. 2002 · 2002
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Mining and summarizing customer reviews
Minqing Hu and Bing Liu. 2004 · 2004
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A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts
Bo Pang and Lillian Lee. 2004 · 2004
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The pascal recognising textual entailment challenge
Ido Dagan, Oren Glickman, and Bernardo Magnini. 2006 · 2005
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Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
Bo Pang and Lillian Lee. 2005 · 2005
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Annotating expressions of opinions and emotions in language
Janyce Wiebe, Theresa Wilson, and Claire Cardie. 2005 · 2005
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Bart: Bayesian additive regression trees
Hugh A Chipman, Edward I George, and Robert E McCulloch. 2010 · 2010
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Learning word vectors for sentiment analysis
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. 2011 · 2011
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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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Good debt or bad debt: Detecting semantic orientations in economic texts
P. Malo, A. Sinha, P. Korhonen, J. Wallenius, and P. Takala. 2014 · 2014
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MPQA 3.0: An entity/event-level sentiment corpus
Lingjia Deng and Janyce Wiebe. 2015 · 2015
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Dbpedia–a large-scale, multilingual knowledge base extracted from wikipedia
Jens Lehmann, Robert Isele, Max Jakob, Anja Jentzsch, Dimitris Kontokostas, Pablo N Mendes, Sebastian Hellmann, Mohamed Morsey, Patrick Van Kleef, Sören Auer, et al. 2015 · 2015
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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015 · 2015
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Xgboost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin. 2016 · 2016
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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CARER: Contextualized affect representations for emotion recognition
Elvis Saravia, Hsien-Chi Toby Liu, Yen-Hao Huang, Junlin Wu, and Yi-Shin Chen. 2018 · 2018
Cutting down on prompts and parameters: Simple few-shot learning with language models
Robert Logan IV, Ivana Balazevic, Eric Wallace, Fabio Petroni, Sameer Singh, and Sebastian Riedel. 2022 · 2022
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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. 2022 · 2022
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Measuring and narrowing the compositionality gap in language models
Ofir Press, Muru Zhang, Sewon Min, Ludwig Schmidt, Noah A. Smith, and Mike Lewis. 2022 · 2022
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Interactive and visual prompt engineering for ad-hoc task adaptation with large language models
Hendrik Strobelt, Albert Webson, Victor Sanh, Benjamin Hoover, Johanna Beyer, Hanspeter Pfister, and Alexander M. Rush. 2022 · 2022
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The commitmentbank: Investigating projection in naturally occurring discourse
Marie-Catherine De Marneffe, Mandy Simons, and Judith Tonhauser. 2019 · 2019
Cited alongside, same era.
Optimal sparse decision trees
Xiyang Hu, Cynthia Rudin, and Margo Seltzer. 2019 · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
Cited alongside, same era.
Interpreting adversarially trained convolutional neural networks
Tianyuan Zhang and Zhanxing Zhu. 2019 · 2019
Cited alongside, same era.
How can we know what language models know?
Zhengbao Jiang, Frank F Xu, Jun Araki, and Graham Neubig. 2020 · 2020
Cited alongside, same era.
How many data points is a prompt worth?
Teven Le Scao and Alexander Rush. 2021 · 2021
Cited alongside, same era.
Yan Shuo Tan, Chandan Singh, Keyan Nasseri, Abhineet Agarwal, and Bin Yu. 2022 · 2022
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Iteratively prompt pre-trained language models for chain of thought
Boshi Wang, Xiang Deng, and Huan Sun. 2022 · 2022
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Describing differences between text distributions with natural language
Ruiqi Zhong, Charlie Snell, Dan Klein, and Jacob Steinhardt. 2022 · 2022
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Graph of thoughts: Solving elaborate problems with large language models
Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Lukas Gianinazzi, Joanna Gajda, Tomasz Lehmann, Michal Podstawski, Hubert Niewiadomski, Piotr Nyczyk, et al. 2023 · 2023
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Langchain: Building applications with llms through composability
Harrison Chase. 2023 · 2023
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Frugalgpt: How to use large language models while reducing cost and improving performance
Lingjiao Chen, Matei Zaharia, and James Zou. 2023 · 2023
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Large language model guided tree-of-thought
Jieyi Long. 2023 · 2023
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Yubo Ma, Yixin Cao, YongChing Hong, and Aixin Sun. 2023 · 2023
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Self-refine: Iterative refinement with self-feedback
Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, Shashank Gupta, Bodhisattwa Prasad Majumder, Katherine Hermann, Sean Welleck, Amir Yazdanbakhsh, and Peter Clark. 2023 · 2023
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On the risks of stealing the decoding algorithms of language models
Ali Naseh, Kalpesh Krishna, Mohit Iyyer, and Amir Houmansadr. 2023 · 2023
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OpenAI. 2023 · 2023
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Boosted prompt ensembles for large language models
Silviu Pitis, Michael R Zhang, Andrew Wang, and Jimmy Ba. 2023 · 2023
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Mini chain: A tiny library for coding with large language models
Alexander M. Rush. 2023 · 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 · 2023
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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
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