Chain-of-ThoughtsPapers
A curated list of papers exploring chain-of-thought reasoning in large language models.
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Decision brief
Chain-of-ThoughtsPapers curates critical research on chain-of-thought reasoning in large language models, aimed at enhancing a model's ability to perform logical reasoning through iterative step-by-step analyses.
Good fit when
- When you need insights into foundational and cutting-edge research on how language models can be prompted or structured to reason logically.
- If your project involves improving the interpretability of reasoning processes in large language models, particularly through methods like Chain of Thought Prompting and Self-Consistency techniques.
Avoid when
- If your focus is on unrelated areas such as image processing or speech recognition, where chain-of-thought reasoning in LLMs does not directly play a role.
- This repository focuses on research and theoretical foundations, not ready-to-use software libraries or codebases, making it less suitable for projects that require immediate practical coding implementations.
Observed Jul 11, 2026 · Source: enrich:decision_facts
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git clone https://github.com/Timothyxxx/Chain-of-ThoughtsPapersSimilar tools
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Overview
This repository compiles research on enhancing the ability of large language models to reason through a series of logical steps or chains of thought, encompassing studies like the Chain of Thought Prompting and PaLM: Scaling Language Modeling with Pathways.
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Chain-of-ThoughtsPapers
A trend starts from "Chain of Thought Prompting Elicits Reasoning in Large Language Models".
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Papers
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Chain of Thought Prompting Elicits Reasoning in Large Language Models.
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Chi, Quoc Le, Denny Zhou [pdf] 2022.1
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Self-Consistency Improves Chain of Thought Reasoning in Language Models.
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Denny Zhou [pdf] 2022.3
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STaR: Self-Taught Reasoner Bootstrapping Reasoning With Reasoning.
Eric Zelikman, Yuhuai Wu, Noah D. Goodman [pdf], [code] 2022.3
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PaLM: Scaling Language Modeling with Pathways.
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, Parker Schuh, Kensen Shi, Sasha Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Yi Tay, Noam Shazeer, Vinodkumar Prabhakaran, Emily Reif, Nan Du, Ben Hutchinson, Reiner Pope, James Bradbury, Jacob Austin, Michael Isard, Guy Gur-Ari, Pengcheng Yin, Toju Duke, Anselm Levskaya, Sanjay Ghemawat, Sunipa Dev, Henryk Michalewski, Xavier Garcia, Vedant Misra, Kevin Robinson, Liam Fedus, Denny Zhou, Daphne Ippolito, David Luan, Hyeontaek Lim, Barret Zoph, Alexander Spiridonov, Ryan Sepassi, David Dohan, Shivani Agrawal, Mark Omernick, Andrew M. Dai, Thanumalayan Sankaranarayana Pillai, Marie Pellat, Aitor Lewkowycz, Erica Moreira, Rewon Child, Oleksandr Polozov, Katherine Lee, Zongwei Zhou, Xuezhi Wang, Brennan Saeta, Mark Diaz, Orhan Firat, Michele Catasta, Jason Wei, Kathy Meier-Hellstern, Douglas Eck, Jeff Dean, Slav Petrov, Noah Fiedel [pdf] 2022.4
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Can language models learn from explanations in context?.
Andrew K. Lampinen, Ishita Dasgupta, Stephanie C. Y. Chan, Kory Matthewson, Michael Henry Tessler, Antonia Creswell, James L. McClelland, Jane X. Wang, Felix Hill [pdf] 2022.4
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Inferring Implicit Relations with Language Models.
Uri Katz, Mor Geva, Jonathan Berant [pdf] 2022.4
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The Unreliability of Explanations in Few-Shot In-Context Learning.
Xi Ye, Greg Durrett [pdf] 2022.5
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Large Language Models are Zero-Shot Reasoners.
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, Yusuke Iwasawa [pdf], [code] 2022.5
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Least-to-Most Prompting Enables Complex Reasoning in Large Language Models.
Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Olivier Bousquet, Quoc Le, Ed Chi [pdf] 2022.5
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Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning.
Antonia Creswell, Murray Shanahan, Irina Higgins [pdf] 2022.5
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On the Advance of Making Language Models Better Reasoners.
Yifei Li, Zeqi Lin, Shizhuo Zhang, Qiang Fu, Bei Chen, Jian-Guang Lou, Weizhu Chen [pdf] 2022.6
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Emergent Abilities of Large Language Models.
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, Ed H. Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, William Fedus [pdf] 2022.6
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