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To enhance the reasoning capabilities of large language models (LLMs), self-consistency has become a popular approach, combining multiple samplings with majority voting.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Solving general arithmetic word problems
Subhro Roy and Dan Roth · 2015
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Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al · 2016
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Long text generation via adversarial training with leaked information
Jiaxian Guo, Sidi Lu, Han Cai, Weinan Zhang, Yong Yu, and Jun Wang · 2018
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Giving bert a calculator: Finding operations and arguments with reading comprehension
Daniel Andor, Luheng He, Kenton Lee, and Emily Pitler · 2019
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Numnet: Machine reading comprehension with numerical reasoning
Qiu Ran, Yankai Lin, Peng Li, Jie Zhou, and Zhiyuan Liu · 2019
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Injecting numerical reasoning skills into language models
Mor Geva, Ankit Gupta, and Jonathan Berant · 2020
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A diverse corpus for evaluating and developing english math word problem solvers
Shen-Yun Miao, Chao-Chun Liang, and Keh-Yih Su · 2020
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Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al · 2021
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Did aristotle use a laptop? a question answering benchmark with implicit reasoning strategies
Mor Geva, Daniel Khashabi, Elad Segal, Tushar Khot, Dan Roth, and Jonathan Berant · 2021
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Are nlp models really able to solve simple math word problems?
Arkil Patel, Satwik Bhattamishra, and Navin Goyal · 2021
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Measuring and improving bert’s mathematical abilities by predicting the order of reasoning
Piotr Piekos, Mateusz Malinowski, and Henryk Michalewski · 2021
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Scaling language models: Methods, analysis & insights from training gopher
Jack W Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican, Jordan Hoffmann, Francis Song, John Aslanides, Sarah Henderson, Roman Ring, Susannah Young, et al · 2021
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Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W Cohen · 2022
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Reflexion: an autonomous agent with dynamic memory and self-reflection
Noah Shinn, Beck Labash, and Ashwin Gopinath · 2023
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Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, et al · 2023
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Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V Le, Ed H Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou · 2023
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Coder reviewer reranking for code generation
Tianyi Zhang, Tao Yu, Tatsunori B Hashimoto, Mike Lewis, Wen-tau Yih, Daniel Fried, and Sida I Wang · 2023
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Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al · 2024
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Yifei Li, Zeqi Lin, Shizhuo Zhang, Qiang Fu, Bei Chen, Jian-Guang Lou, and Weizhu Chen · 2022
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
Cited alongside, same era.
Let’s sample step by step: Adaptive-consistency for efficient reasoning and coding with llms
Pranjal Aggarwal, Aman Madaan, Yiming Yang, et al · 2023
Cited alongside, same era.
Specializing smaller language models towards multi-step reasoning
Yao Fu, Hao Peng, Litu Ou, Ashish Sabharwal, and Tushar Khot · 2023
Cited alongside, same era.
Large language models are reasoning teachers
Namgyu Ho, Laura Schmid, and Seyoung Yun · 2023
Cited alongside, same era.
Symbolic chain-of-thought distillation: Small models can also “think” step-by-step
Liunian Harold Li, Jack Hessel, Youngjae Yu, Xiang Ren, Kai Wei Chang, and Yejin Choi · 2023
Cited alongside, same era.
Teaching small language models to reason
Lucie Charlotte Magister, Jonathan Mallinson, Jakub Adamek, Eric Malmi, and Aliaksei Severyn · 2023
Cited alongside, same era.
Closest in time.
Escape sky-high cost: Early-stopping self-consistency for multi-step reasoning
Yiwei Li, Peiwen Yuan, Shaoxiong Feng, Boyuan Pan, Xinglin Wang, Bin Sun, Heda Wang, and Kan Li · 2024
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Aixin Liu, Bei Feng, Bing Xue, Bingxuan Wang, Bochao Wu, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, et al · 2024
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Self-refine: Iterative refinement with self-feedback
Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, et al · 2024
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Refiner: Reasoning feedback on intermediate representations
Debjit Paul, Mete Ismayilzada, Maxime Peyrard, Beatriz Borges, Antoine Bosselut, Robert West, and Boi Faltings · 2024
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Self-evaluation guided beam search for reasoning
Yuxi Xie, Kenji Kawaguchi, Yiran Zhao, James Xu Zhao, Min-Yen Kan, Junxian He, and Michael Xie · 2024
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Deductive beam search: Decoding deducible rationale for chain-of-thought reasoning
Tinghui Zhu, Kai Zhang, Jian Xie, and Yu Su · 2024
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