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This paper provides an introductory survey to GPT-3.
Language and machines: Computers in translation and linguistics
John R. Pierce and John B. Carroll · 1966
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Machine translation: A brief history
William J. Hutchins · 1995
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Attention is all you need
Ashish Vaswani, Noam M. Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Deep neural solver for math word problems
Yan Wang, Xiaojiang Liu, and Shuming Shi · 2017
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 2018
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The history and promise of machine translation
Lane Schwartz · 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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Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
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Language models are few-shot learners
Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Earlier work this paper cites.
Gpt-3: Its nature, scope, limits, and consequences
Luciano Floridi and Massimo Chiriatti · 2020
Earlier work this paper cites.
A knowledge-aware sequence-to-tree network for math word problem solving
Qinzhuo Wu, Qi Zhang, Jinlan Fu, and Xuan-Jing Huang · 2020
Earlier work this paper cites.
Graph-to-tree learning for solving math word problems
Jipeng Zhang, Lei Wang, Roy Ka-Wei Lee, Yi Bin, Yan Wang, Jie Shao, and Ee-Peng Lim · 2020
Cited alongside, same era.
Persistent anti-muslim bias in large language models
Abubakar Abid, Maheen Saleem Farooqi, and James Y. Zou · 2021
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On the dangers of stochastic parrots: Can language models be too big? ´f99c
Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell · 2021
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Medically aware gpt-3 as a data generator for medical dialogue summarization
Bharath Chintagunta, Namit Katariya, Xavier Amatriain, and Anitha Kannan · 2021
Cited alongside, same era.
Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman · 2021
Cited alongside, same era.
Gpt3-to-plan: Extracting plans from text using gpt-3
Alberto Olmo, Sarath Sreedharan, and S. Kambhampati · 2021
Later among the works it cites.
Prompt scoring system for dialogue summarization using gpt-3
George Prodan and Elena Pelican · 2021
Later among the works it cites.
Recurrent neural networks (rnn)
Cao Xiao and Jimeng Sun · 2021
Later among the works it cites.
Is gpt-3 text indistinguishable from human text? scarecrow: A framework for scrutinizing machine text
Yao Dou, Maxwell Forbes, Rik Koncel-Kedziorski, Noah A. Smith, and Yejin Choi · 2022
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How do we audit generative algorithms?
KATY ILONKA GERO · 2022
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New meaning for nlp: the trials and tribulations of natural language processing with gpt-3 in ophthalmology
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Plagiarism in the age of massive generative pre-trained transformers (gpt-3)
Nassim Dehouche · 2021
Cited alongside, same era.
Considering the possibilities and pitfalls of generative pre-trained transformer 3 (gpt-3) in healthcare delivery
Diane M. Korngiebel and Sean D. Mooney · 2021
Cited alongside, same era.
Gender and representation bias in gpt-3 generated stories
Li Lucy and David Bamman · 2021
Cited alongside, same era.
Q-pain: A question answering dataset to measure social bias in pain management
C’ecile Log’e, Emily L. Ross, David Yaw Amoah Dadey, Saahil Jain, Adriel Saporta, Andrew Y. Ng, and Pranav Rajpurkar · 2021
Cited alongside, same era.
Tianyang Lin, Yuxin Wang, Xiangyang Liu, and Xipeng Qiu · 2021
Cited alongside, same era.
Cgems: A metric model for automatic code generation using gpt-3
Aishwarya Narasimhan, Krishna Prasad Agara Venkatesha Rao, and B VeenaM · 2021
Cited alongside, same era.
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, Erik Brynjolfsson, S. Buch, Dallas Card, Rodrigo Castellon, Niladri S. Chatterji, Annie S. Chen, Kathleen A. Creel, Jared Davis, Dora Demszky, Chris Donahue, Moussa Doumbouya, Esin Durmus, Stefano Ermon, John Etchemendy, Kawin Ethayarajh, Li Fei-Fei, Chelsea Finn, Trevor Gale, Lauren E. Gillespie, Karan Goel, Noah D. Goodman, Shelby Grossman, Neel Guha, Tatsunori Hashimoto, Peter Henderson, John Hewitt, Daniel E. Ho, Jenny Hong, Kyle Hsu, Jing Huang, Thomas F. Icard, Saahil Jain, Dan Jurafsky, Pratyusha Kalluri, Siddharth Karamcheti, Geoff Keeling, Fereshte Khani, O. Khattab, Pang Wei Koh, Mark S. Krass, Ranjay Krishna, Rohith Kuditipudi, Ananya Kumar, Faisal Ladhak, Mina Lee, Tony Lee, Jure Leskovec, Isabelle Levent, Xiang Lisa Li, Xuechen Li, Tengyu Ma, Ali Malik, Christopher D. Manning, Suvir P. Mirchandani, Eric Mitchell, Zanele Munyikwa, Suraj Nair, Avanika Narayan, Deepak Narayanan, Benjamin Newman, Allen Nie, Juan Carlos Niebles, Hamed Nilforoshan, J. F. Nyarko, Giray Ogut, Laurel J. Orr, Isabel Papadimitriou, Joon Sung Park, Chris Piech, Eva Portelance, Christopher Potts, Aditi Raghunathan, Robert Reich, Hongyu Ren, Frieda Rong, Yusuf H. Roohani, Camilo Ruiz, Jack Ryan, Christopher R’e, Dorsa Sadigh, Shiori Sagawa, Keshav Santhanam, Andy Shih, Krishna Parasuram Srinivasan, Alex Tamkin, Rohan Taori, Armin W. Thomas, Florian Tramèr, Rose E. Wang, William Wang, Bohan Wu, Jiajun Wu, Yuhuai Wu, Sang Michael Xie, Michihiro Yasunaga, Jiaxuan You, Matei A. Zaharia, Michael Zhang, Tianyi Zhang, Xikun Zhang, Yuhui Zhang, Lucia Zheng, Kaitlyn Zhou, and Percy Liang
Cited in the paper.
Siddharth Nath, Abdullah Marie, Simon Ellershaw, Edward Korot, and Pearse A. Keane · 2022
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Brilliance bias in gpt-3
Juliana Shihadeh, Margareta Ackerman, Ashley Troske, Nicole Lawson, and Edith Gonzalez · 2022
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Operationalizing and implementing pretrained, large artificial intelligence linguistic models in the us health care system: Outlook of generative pretrained transformer 3 (gpt-3) as a service model
Emre Sezgin, Joseph W. Sirrianni, and Simon Lin Linwood · 2022
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Lesson plan generation using natural language processing: Prompting best practices with openai’s gpt-3 model (poster 27)
Joel Walsh · 2022
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Solving math word problems concerning systems of equations with gpt-3
Mingyu Zong and Bhaskar Krishnamachari · 2022
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