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Chain-of-ThoughtsPapers

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A curated list of papers exploring chain-of-thought reasoning in large language models.

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Created Apr 6, 2022

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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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Categories

Model TrainingLLM Frameworks

Tags

gpt-3chain-of-thoughtlarge-language-modelsprompt-learningcodexin-context-learningpalm

README

Chain-of-ThoughtsPapers

A trend starts from "Chain of Thought Prompting Elicits Reasoning in Large Language Models".

Check Tool use LLMs and Environment Interactive LLMs for the newest good direction we are doing!

Papers

  1. 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

  2. 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

  3. STaR: Self-Taught Reasoner Bootstrapping Reasoning With Reasoning.

    Eric Zelikman, Yuhuai Wu, Noah D. Goodman [pdf], [code] 2022.3

  4. 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

  5. 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

  6. Inferring Implicit Relations with Language Models.

    Uri Katz, Mor Geva, Jonathan Berant [pdf] 2022.4

  7. The Unreliability of Explanations in Few-Shot In-Context Learning.

    Xi Ye, Greg Durrett [pdf] 2022.5

  8. Large Language Models are Zero-Shot Reasoners.

    Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, Yusuke Iwasawa [pdf], [code] 2022.5

  9. 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

  10. Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning.

    Antonia Creswell, Murray Shanahan, Irina Higgins [pdf] 2022.5

  11. 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

  12. 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

  13. **