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Large language models (LLMs) enable system builders today to create competent NLP systems through prompting, where they only need to describe the task in natural language and provide a few examples.
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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Gradio: Hassle-free sharing and testing of ML models in the wild
Abubakar Abid, Ali Abdalla, Ali Abid, Dawood Khan, Abdulrahman Alfozan, and James Zou. 2019 · 1906
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A new measure of rank correlation
Maurice G Kendall. 1938 · 1938
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Okapi at trec-4
Stephen E. Robertson, Steve Walker, Micheline Hancock-Beaulieu, Mike Gatford, and A. Payne. 1995 · 1995
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chrF: character n-gram F-score for automatic MT evaluation
Maja Popović. 2015 · 2015
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The goldilocks principle: Reading children’s books with explicit memory representations
Felix Hill, Antoine Bordes, Sumit Chopra, and Jason Weston. 2016 · 2016
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SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Bertscore: Evaluating text generation with bert
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi. 2019 · 2019
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Unsupervised cross-lingual representation learning at scale
Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2020 · 2020
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Gpt-3: Its nature, scope, limits, and consequences
Luciano Floridi and Massimo Chiriatti. 2020 · 2020
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Model zoo
Jing Yu Koh. 2020 · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020 · 2020
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OPUS-MT — Building open translation services for the World
Jörg Tiedemann and Santhosh Thottingal. 2020 · 2020
Cited alongside, same era.
Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi 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 Rush. 2020 · 2020
Cited alongside, same era.
On the dangers of stochastic parrots: Can language models be too big?
Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021 · 2021
Cited alongside, same era.
Recommending datasets for scientific problem descriptions
Michael Färber and Ann-Kathrin Leisinger. 2021 · 2021
Cited alongside, same era.
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 · 2021
Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2022 · 2022
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Sparks of artificial general intelligence: Early experiments with gpt-4
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al. 2023 · 2023
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A systematic study of knowledge distillation for natural language generation with pseudo-target training
Nitay Calderon, Subhabrata Mukherjee, Roi Reichart, and Amir Kantor. 2023 · 2023
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Precise zero-shot dense retrieval without relevance labels
Luyu Gao, Xueguang Ma, Jimmy Lin, and Jamie Callan. 2023 · 2023
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The false promise of imitating proprietary llms
Arnav Gudibande, Eric Wallace, Charles Burton Snell, Xinyang Geng, Hao Liu, P. Abbeel, Sergey Levine, and Dawn Song. 2023 · 2023
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Cited alongside, same era.
Prompt programming for large language models: Beyond the few-shot paradigm
Laria Reynolds and Kyle McDonell. 2021 · 2021
Cited alongside, same era.
Want to reduce labeling cost? GPT-3 can help
Shuohang Wang, Yang Liu, Yichong Xu, Chenguang Zhu, and Michael Zeng. 2021a · 2021
Cited alongside, same era.
Promptmaker: Prompt-based prototyping with large language models
Ellen Jiang, Kristen Olson, Edwin Toh, Alejandra Molina, Aaron Donsbach, Michael Terry, and Carrie J Cai. 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. 2022 · 2022
Cited alongside, same era.
Challenges in deploying machine learning: a survey of case studies
Andrei Paleyes, Raoul-Gabriel Urma, and Neil D Lawrence. 2022 · 2022
Cited alongside, same era.
Lut-gemm: Quantized matrix multiplication based on luts for efficient inference in large-scale generative language models
Gunho Park, Baeseong Park, Minsub Kim, Sungjae Lee, Jeonghoon Kim, Beomseok Kwon, Se Jung Kwon, Byeongwook Kim, Youngjoo Lee, and Dongsoo Lee. 2022 · 2022
Cited alongside, same era.
Operationalizing and implementing pretrained large ai linguistic models in the united states healthcare system: An outlook of gpt-3 as a service
Emre Sezgin, Joseph Sirrianni, and Simon L. Linwood. 2022 · 2022
Cited alongside, same era.
Closest in time.
Annollm: Making large language models to be better crowdsourced annotators
Xingwei He, Zheng-Wen Lin, Yeyun Gong, Alex Jin, Hang Zhang, Chen Lin, Jian Jiao, Siu Ming Yiu, Nan Duan, and Weizhu Chen. 2023 · 2023
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Zeno GPT Machine Translation Report
Graham Neubig and Zhiwei He. 2023 · 2023
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Closed ai models make bad baselines
Anna Rogers, Niranjan Balasubramanian, Leon Derczynski, Jesse Dodge, Alexander Koller, Sasha Luccioni, Maarten Sap, Roy Schwartz, Noah A Smith, and Emma Strubell. 2023 · 2023
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DataFinder: Scientific dataset recommendation from natural language descriptions
Vijay Viswanathan, Luyu Gao, Tongshuang Wu, Pengfei Liu, and Graham Neubig. 2023 · 2023
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MCoNaLa: A benchmark for code generation from multiple natural languages
Zhiruo Wang, Grace Cuenca, Shuyan Zhou, Frank F. Xu, and Graham Neubig. 2023 · 2023
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Scattershot: Interactive in-context example curation for text transformation
Sherry Wu, Hua Shen, Daniel S Weld, Jeffrey Heer, and Marco Tulio Ribeiro. 2023 · 2023
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Limits and possibilities for “ethical ai” in open source: A study of deepfakes
David Gray Widder, Dawn Nafus, Laura Dabbish, and James Herbsleb. 2022 · 2046
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