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Prompt design and engineering has rapidly become essential for maximizing the potential of large language models.
Machine learning: The high interest credit card of technical debt
D. Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, and Michael Young · 2014
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Teaching algorithmic reasoning via in-context learning, 2022
Hattie Zhou, Azade Nova, Hugo Larochelle, Aaron Courville, Behnam Neyshabur, and Hanie Sedghi · 2022
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Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity, 2022
Yao Lu, Max Bartolo, Alastair Moore, Sebastian Riedel, and Pontus Stenetorp · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, brian ichter, Fei Xia, Ed Chi, Quoc V Le, and Denny Zhou · 2022
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Automatic chain of thought prompting in large language models, 2022
Zhuosheng Zhang, Aston Zhang, Mu Li, and Alex Smola · 2022
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Promptchainer: Chaining large language model prompts through visual programming, 2022
Tongshuang Wu, Ellen Jiang, Aaron Donsbach, Jeff Gray, Alejandra Molina, Michael Terry, and Carrie J Cai · 2022
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Transformer models: an introduction and catalog, 2023
Xavier Amatriain, Ananth Sankar, Jie Bing, Praveen Kumar Bodigutla, Timothy J. Hazen, and Michaeel Kazi · 2023
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Tree of thoughts: Deliberate problem solving with large language models, 2023
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, and Karthik Narasimhan · 2023
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Toolformer: Language models can teach themselves to use tools, 2023
Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom · 2023
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Gorilla: Large language model connected with massive apis, 2023
Shishir G. Patil, Tianjun Zhang, Xin Wang, and Joseph E. Gonzalez · 2023
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Art: Automatic multi-step reasoning and tool-use for large language models, 2023
Bhargavi Paranjape, Scott Lundberg, Sameer Singh, Hannaneh Hajishirzi, Luke Zettlemoyer, and Marco Tulio Ribeiro · 2023
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Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models, 2023
Potsawee Manakul, Adian Liusie, and Mark J. F. Gales · 2023
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Exploring the mit mathematics and eecs curriculum using large language models, 2023
Sarah J. Zhang, Samuel Florin, Ariel N. Lee, Eamon Niknafs, Andrei Marginean, Annie Wang, Keith Tyser, Zad Chin, Yann Hicke, Nikhil Singh, Madeleine Udell, Yoon Kim, Tonio Buonassisi, Armando Solar-Lezama, and Iddo Drori · 2023
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Large language models are human-level prompt engineers, 2023
Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, and Jimmy Ba · 2023
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Question answering using retrieval augmented generation with foundation models in amazon sagemaker jumpstart, 2023
Amazon Web Services · 2023
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Unifying large language models and knowledge graphs: A roadmap
Shirui Pan, Linhao Luo, Yufei Wang, Chen Chen, Jiapu Wang, and Xindong Wu · 2023
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Retrieval-augmented generation for large language models: A survey
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Reflexion: Language agents with verbal reinforcement learning, 2023
Noah Shinn, Federico Cassano, Edward Berman, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao · 2023
Cited alongside, same era.
Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, and Haofen Wang · 2023
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Measuring and mitigating hallucinations in large language models: A multifaceted approach, 2024
Xavier Amatriain · 2024
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