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Few-shot Chain-of-Thought (CoT) prompting has demonstrated strong performance in improving the reasoning capabilities of large language models (LLMs).
Language models are few-shot learners
Tom B Brown · 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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What learning algorithm is in-context learning? investigations with linear models
Ekin Akyürek, Dale Schuurmans, Jacob Andreas, Tengyu Ma, and Denny Zhou · 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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Text and patterns: For effective chain of thought, it takes two to tango
Aman Madaan and Amir Yazdanbakhsh · 2022
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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 · 2022
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Towards understanding chain-of-thought prompting: An empirical study of what matters
Boshi Wang, Sewon Min, Xiang Deng, Jiaming Shen, You Wu, Luke Zettlemoyer, and Huan Sun · 2022
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Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou · 2022
Earlier work this paper cites.
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
Earlier work this paper cites.
Automatic chain of thought prompting in large language models
Zhuosheng Zhang, Aston Zhang, Mu Li, and Alex Smola · 2022
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Transformers learn to implement preconditioned gradient descent for in-context learning
Kwangjun Ahn, Xiang Cheng, Hadi Daneshmand, and Suvrit Sra · 2023
Earlier work this paper cites.
Transformers as statisticians: Provable in-context learning with in-context algorithm selection
Yu Bai, Fan Chen, Huan Wang, Caiming Xiong, and Song Mei · 2023
Cited alongside, same era.
Graph of thoughts: Solving elaborate problems with large language models
Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Lukas Gianinazzi, Joanna Gajda, Tomasz Lehmann, Michal Podstawski, Hubert Niewiadomski, Piotr Nyczyk, et al · 2023
Cited alongside, same era.
Transformers implement functional gradient descent to learn non-linear functions in context
Xiang Cheng, Yuxin Chen, and Suvrit Sra · 2023
Cited alongside, same era.
Towards revealing the mystery behind chain of thought: a theoretical perspective
Guhao Feng, Yuntian Gu, Bohang Zhang, Haotian Ye, Di He, and Liwei Wang · 2023
Cited alongside, same era.
Skyler Wu, Eric Meng Shen, Charumathi Badrinath, Jiaqi Ma, and Himabindu Lakkaraju · 2023
Later among the works it cites.
Tree of thoughts: Deliberate problem solving with large language models
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L Griffiths, Yuan Cao, and Karthik Narasimhan · 2023
Later among the works it cites.
Trained transformers learn linear models in-context
Ruiqi Zhang, Spencer Frei, and Peter L Bartlett · 2023
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Deepseek llm: Scaling open-source language models with longtermism
Xiao Bi, Deli Chen, Guanting Chen, Shanhuang Chen, Damai Dai, Chengqi Deng, Honghui Ding, Kai Dong, Qiushi Du, Zhe Fu, et al · 2024
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Yao Fu, Litu Ou, Mingyu Chen, Yuhao Wan, Hao Peng, and Tushar Khot · 2023
Cited alongside, same era.
In-context convergence of transformers
Yu Huang, Yuan Cheng, and Yingbin Liang · 2023
Cited alongside, same era.
Dissecting chain-of-thought: A study on compositional in-context learning of mlps
Yingcong Li, Kartik Sreenivasan, Angeliki Giannou, Dimitris Papailiopoulos, and Samet Oymak · 2023
Cited alongside, same era.
Faithful chain-of-thought reasoning
Qing Lyu, Shreya Havaldar, Adam Stein, Li Zhang, Delip Rao, Eric Wong, Marianna Apidianaki, and Chris Callison-Burch · 2023
Cited alongside, same era.
Gemini: a family of highly capable multimodal models
Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al · 2023
Cited alongside, same era.
Why can large language models generate correct chain-of-thoughts?
Rasul Tutunov, Antoine Grosnit, Juliusz Ziomek, Jun Wang, and Haitham Bou-Ammar · 2023
Cited alongside, same era.
Transformers learn in-context by gradient descent
Johannes Von Oswald, Eyvind Niklasson, Ettore Randazzo, João Sacramento, Alexander Mordvintsev, Andrey Zhmoginov, and Max Vladymyrov · 2023
Cited alongside, same era.
Siyu Chen, Heejune Sheen, Tianhao Wang, and Zhuoran Yang · 2024
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Superiority of multi-head attention in in-context linear regression
Yingqian Cui, Jie Ren, Pengfei He, Jiliang Tang, and Yue Xing · 2024
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The impact of reasoning step length on large language models
Mingyu Jin, Qinkai Yu, Haiyan Zhao, Wenyue Hua, Yanda Meng, Yongfeng Zhang, Mengnan Du, et al · 2024
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Chain of thought empowers transformers to solve inherently serial problems
Zhiyuan Li, Hong Liu, Denny Zhou, and Tengyu Ma · 2024
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Why think step by step? reasoning emerges from the locality of experience
Ben Prystawski, Michael Li, and Noah Goodman · 2024
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Benefits of transformer: In-context learning in linear regression tasks with unstructured data
Yue Xing, Xiaofeng Lin, Namjoon Suh, Qifan Song, and Guang Cheng · 2024
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