Towards Understanding Chain-of-Thought Prompting: An Empirical Study of
What Matters

Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters

20 December 2022

Luke Zettlemoyer

Huan Sun

Papers citing "Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters"

8 / 58 papers shown

Title
Large Language Models Can Self-Improve Jiaxin Huang S. Gu Le Hou Yuexin Wu Xuezhi Wang Hongkun Yu Jiawei Han ReLM AI4MH LRM 47 564 0 20 Oct 2022
Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought Abulhair Saparov He He ELM LRM ReLM 123 277 0 03 Oct 2022
Can Large Language Models Truly Understand Prompts? A Case Study with Negated Prompts Joel Jang Seonghyeon Ye Minjoon Seo ELM LRM 95 64 0 26 Sep 2022
Text and Patterns: For Effective Chain of Thought, It Takes Two to Tango Aman Madaan Amir Yazdanbakhsh LRM 151 116 0 16 Sep 2022
Large Language Models are Zero-Shot Reasoners Takeshi Kojima S. Gu Machel Reid Yutaka Matsuo Yusuke Iwasawa ReLM LRM 328 4,077 0 24 May 2022
Self-Consistency Improves Chain of Thought Reasoning in Language Models Xuezhi Wang Jason W. Wei Dale Schuurmans Quoc Le Ed H. Chi Sharan Narang Aakanksha Chowdhery Denny Zhou ReLM BDL LRM AI4CE 314 3,273 0 21 Mar 2022
Training language models to follow instructions with human feedback Long Ouyang Jeff Wu Xu Jiang Diogo Almeida Carroll L. Wainwright ... Amanda Askell Peter Welinder Paul Christiano Jan Leike Ryan J. Lowe OSLM ALM 339 12,003 0 04 Mar 2022
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models Jason W. Wei Xuezhi Wang Dale Schuurmans Maarten Bosma Brian Ichter F. Xia Ed H. Chi Quoc Le Denny Zhou LM&Ro LRM AI4CE ReLM 395 8,559 0 28 Jan 2022