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Prompt Engineering (PE) has emerged as a critical technique for guiding Large Language Models (LLMs) in solving intricate tasks.
Natural language programming: Styles, strategies, and contrasts
Lance A Miller · 1981
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Learning and development in neural networks: The importance of starting small
Jeffrey L Elman · 1993
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Popular ensemble methods: An empirical study
D. Opitz and R. Maclin · 1999
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Optimal control theory: an introduction
Donald E Kirk · 2004
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Nlp (natural language processing) for nlp (natural language programming)
Rada Mihalcea, Hugo Liu, and Henry Lieberman · 2006
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Ensemble based systems in decision making
Robi Polikar · 2006
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Ensemble-based classifiers
Lior Rokach · 2010
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Language models are few-shot learners, 2020
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared 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 M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher 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
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Aragpt2: Pre-trained transformer for arabic language generation
Wissam Antoun, Fady Baly, and Hazem Hajj · 2020
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Growing action spaces
Gregory Farquhar, Laura Gustafson, Zeming Lin, Shimon Whiteson, Nicolas Usunier, and Gabriel Synnaeve · 2020
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Cutting down on prompts and parameters: Simple few-shot learning with language models, 2021
Robert L. Logan IV au2, Ivana Balažević, Eric Wallace, Fabio Petroni, Sameer Singh, and Sebastian Riedel · 2021
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Large language models are human-level prompt engineers
Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, and Jimmy Ba · 2022
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Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Huai hsin Chi, F. Xia, Quoc 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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Pal: Program-aided language models
Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, and Graham Neubig · 2022
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Generated knowledge prompting for commonsense reasoning, 2022
Jiacheng Liu, Alisa Liu, Ximing Lu, Sean Welleck, Peter West, Ronan Le Bras, Yejin Choi, and Hannaneh Hajishirzi · 2022
Cited alongside, same era.
Maieutic prompting: Logically consistent reasoning with recursive explanations, 2022
Jaehun Jung, Lianhui Qin, Sean Welleck, Faeze Brahman, Chandra Bhagavatula, Ronan Le Bras, and Yejin Choi · 2022
Cited alongside, same era.
Program of thoughts prompting: Disentangling computation from reasoning for numerical reasoning tasks, 2022
Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W. Cohen · 2022
Cited alongside, same era.
Rlprompt: Optimizing discrete text prompts with reinforcement learning, 2022
Mingkai Deng, Jianyu Wang, Cheng-Ping Hsieh, Yihan Wang, Han Guo, Tianmin Shu, Meng Song, Eric P. Xing, and Zhiting Hu · 2022
Cited alongside, same era.
Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Huai hsin Chi, and Denny Zhou · 2022
Prompt space optimizing few-shot reasoning success with large language models
Fobo Shi, Peijun Qing, D. Yang, Nan Wang, Youbo Lei, H. Lu, and Xiaodong Lin · 2023
Closest in time.
Mint: Evaluating llms in multi-turn interaction with tools and language feedback, 2023
Xingyao Wang, Zihan Wang, Jiateng Liu, Yangyi Chen, Lifan Yuan, Hao Peng, and Heng Ji · 2023
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The AI revolution in medicine: GPT-4 and beyond
Peter Lee, Carey Goldberg, and Isaac Kohane · 2023
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Why can gpt learn in-context? language models implicitly perform gradient descent as meta-optimizers, 2023
Damai Dai, Yutao Sun, Li Dong, Yaru Hao, Shuming Ma, Zhifang Sui, and Furu Wei · 2023
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Large language models are better reasoners with self-verification
Yixuan Weng, Minjun Zhu, Fei Xia, Bin Li, Shizhu He, Kang Liu, and Jun Zhao · 2023
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Cited alongside, same era.
An information-theoretic approach to prompt engineering without ground truth labels
Taylor Sorensen, Joshua Robinson, Christopher Rytting, Alexander Shaw, Kyle Rogers, Alexia Delorey, Mahmoud Khalil, Nancy Fulda, and David Wingate · 2022
Cited alongside, same era.
A brief history of prompt: Leveraging language models, 2023
Golam Md Muktadir · 2023
Cited alongside, same era.
Least-to-most prompting enables complex reasoning in large language models, 2023
Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc Le, and Ed Chi · 2023
Cited alongside, same era.
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
Cited alongside, same era.
Graph of thoughts: Solving elaborate problems with large language models, 2023
Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Lukas Gianinazzi, Joanna Gajda, Tomasz Lehmann, Michal Podstawski, Hubert Niewiadomski, Piotr Nyczyk, and Torsten Hoefler · 2023
Cited alongside, same era.
Large language models are zero-shot reasoners, 2023
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa · 2023
Cited alongside, same era.
Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig · 2023
Cited alongside, same era.
Chuanyang Zheng, Zhengying Liu, Enze Xie, Zhenguo Li, and Yu Li · 2023
Closest in time.
Selfzcot: a self-prompt zero-shot cot from semantic-level to code-level for a better utilization of llms, 2023
IokTong Lei and ZhiDong Deng · 2023
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Dynamic prompt learning via policy gradient for semi-structured mathematical reasoning, 2023
Pan Lu, Liang Qiu, Kai-Wei Chang, Ying Nian Wu, Song-Chun Zhu, Tanmay Rajpurohit, Peter Clark, and Ashwin Kalyan · 2023
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Complexity-based prompting for multi-step reasoning, 2023
Yao Fu, Hao Peng, Ashish Sabharwal, Peter Clark, and Tushar Khot · 2023
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Making large language models better reasoners with step-aware verifier, 2023
Yifei Li, Zeqi Lin, Shizhuo Zhang, Qiang Fu, Bei Chen, Jian-Guang Lou, and Weizhu Chen · 2023
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Mathprompter: Mathematical reasoning using large language models
Shima Imani, Liang Du, and H. Shrivastava · 2023
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Communicative agents for software development, 2023
Chen Qian, Xin Cong, Wei Liu, Cheng Yang, Weize Chen, Yusheng Su, Yufan Dang, Jiahao Li, Juyuan Xu, Dahai Li, Zhiyuan Liu, and Maosong Sun · 2023
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Improving factuality and reasoning in language models through multiagent debate, 2023
Yilun Du, Shuang Li, Antonio Torralba, Joshua B. Tenenbaum, and Igor Mordatch · 2023
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Improving language model negotiation with self-play and in-context learning from ai feedback, 2023
Yao Fu, Hao Peng, Tushar Khot, and Mirella Lapata · 2023
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Exploring the intersection of large language models and agent-based modeling via prompt engineering, 2023
Edward Junprung · 2023
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