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Distilling explicit chain-of-thought reasoning paths has emerged as an effective method for improving the reasoning abilities of large language models (LLMs) across various tasks.
Model compression
Cristian Bucila, Rich Caruana, and Alexandru Niculescu-Mizil · 2006
Earlier work this paper cites.
Distilling the knowledge in a neural network, 2015
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Deepcoder: Learning to write programs
Matej Balog, Alexander L. Gaunt, Marc Brockschmidt, Sebastian Nowozin, and Daniel Tarlow · 2017
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Neural program search: Solving programming tasks from description and examples
Illia Polosukhin and Alexander Skidanov · 2018
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Learning to mine aligned code and natural language pairs from stack overflow, 2018
Pengcheng Yin, Bowen Deng, Edgar Chen, Bogdan Vasilescu, and Graham Neubig · 2018
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Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Pondé de Oliveira Pinto, Jared Kaplan, Harrison Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian, Clemens Winter, Philippe Tillet, Felipe Petroski Such, Dave Cummings, Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert-Voss, William Hebgen Guss, Alex Nichol, Alex Paino, Nikolas Tezak, Jie Tang, Igor Babuschkin, Suchir Balaji, Shantanu Jain, William Saunders, Christopher Hesse, Andrew N. Carr, Jan Leike, Joshua Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob McGrew, Dario Amodei, Sam McCandlish, Ilya Sutskever, and Wojciech Zaremba · 2021
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Measuring coding challenge competence with apps, 2021
Dan Hendrycks, Steven Basart, Saurav Kadavath, Mantas Mazeika, Akul Arora, Ethan Guo, Collin Burns, Samir Puranik, Horace He, Dawn Song, and Jacob Steinhardt · 2021
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Program of thoughts prompting: Disentangling computation from reasoning for numerical reasoning tasks, 2022
Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W. Cohen · 2022
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System 1 + system 2 = better world: Neural-symbolic chain of logic reasoning
Wenyue Hua and Yongfeng Zhang · 2022
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Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa · 2022
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Competition-level code generation with AlphaCode
Yujia Li, David Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Ré mi Leblond, Tom Eccles, James Keeling, Felix Gimeno, Agustin Dal Lago, Thomas Hubert, Peter Choy, Cyprien de Masson d’Autume, Igor Babuschkin, Xinyun Chen, Po-Sen Huang, Johannes Welbl, Sven Gowal, Alexey Cherepanov, James Molloy, Daniel J. Mankowitz, Esme Sutherland Robson, Pushmeet Kohli, Nando de Freitas, Koray Kavukcuoglu, and Oriol Vinyals · 2022
Cited alongside, same era.
Iteratively prompt pre-trained language models for chain of thought
Boshi Wang, Xiang Deng, and Huan Sun · 2022
Cited alongside, same era.
Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Chi, Quoc Le, and Denny Zhou · 2022
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Teaching large language models to self-debug, 2023
Xinyun Chen, Maxwell Lin, Nathanael Schärli, and Denny Zhou · 2023
Cited alongside, same era.
Distilling step-by-step! outperforming larger language models with less training data and smaller model sizes, 2023
Alpaca: A strong, replicable instruction-following model
Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto · 2023
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Effective distillation of table-based reasoning ability from llms, 2023
Bohao Yang, Chen Tang, Kun Zhao, Chenghao Xiao, and Chenghua Lin · 2023
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Mammoth: Building math generalist models through hybrid instruction tuning, 2023
Xiang Yue, Xingwei Qu, Ge Zhang, Yao Fu, Wenhao Huang, Huan Sun, Yu Su, and Wenhu Chen · 2023
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Parsel: A (de-)compositional framework for algorithmic reasoning with language models, 2023
Eric Zelikman, Qian Huang, Gabriel Poesia, Noah D. Goodman, and Nick Haber · 2023
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Algo: Synthesizing algorithmic programs with llm-generated oracle verifiers, 2023
Kexun Zhang, Danqing Wang, Jingtao Xia, William Yang Wang, and Lei Li · 2023
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Cheng-Yu Hsieh, Chun-Liang Li, Chih-Kuan Yeh, Hootan Nakhost, Yasuhisa Fujii, Alexander Ratner, Ranjay Krishna, Chen-Yu Lee, and Tomas Pfister · 2023
Cited alongside, same era.
Competition-level problems are effective llm evaluators, 2023
Yiming Huang, Zhenghao Lin, Xiao Liu, Yeyun Gong, Shuai Lu, Fangyu Lei, Yaobo Liang, Yelong Shen, Chen Lin, Nan Duan, and Weizhu Chen · 2023
Cited alongside, same era.
Explaining competitive-level programming solutions using llms, 2023
Jierui Li, Szymon Tworkowski, Yingying Wu, and Raymond Mooney · 2023
Cited alongside, same era.
Faithful chain-of-thought reasoning, 2023
Qing Lyu, Shreya Havaldar, Adam Stein, Li Zhang, Delip Rao, Eric Wong, Marianna Apidianaki, and Chris Callison-Burch · 2023
Cited alongside, same era.
Is self-repair a silver bullet for code generation?, 2023
Theo X. Olausson, Jeevana Priya Inala, Chenglong Wang, Jianfeng Gao, and Armando Solar-Lezama · 2023
Cited alongside, same era.
ChatGPT: Optimizing Language Models for Dialogue
OpenAI
Cited in the paper.
Gpt-4 technical report, 2023b
OpenAI
Cited in the paper.
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
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Pad: Program-aided distillation specializes large models in reasoning, 2023
Xuekai Zhu, Biqing Qi, Kaiyan Zhang, Xingwei Long, and Bowen Zhou · 2023
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Deepseek-coder: When the large language model meets programming – the rise of code intelligence, 2024
Daya Guo, Qihao Zhu, Dejian Yang, Zhenda Xie, Kai Dong, Wentao Zhang, Guanting Chen, Xiao Bi, Y. Wu, Y. K. Li, Fuli Luo, Yingfei Xiong, and Wenfeng Liang · 2024
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Code generation with alphacodium: From prompt engineering to flow engineering, 2024
Tal Ridnik, Dedy Kredo, and Itamar Friedman · 2024
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