Deciphering the Factors Influencing the Efficacy of Chain-of-Thought: Probability, Memorization, and Noisy Reasoning

1 July 2024

Papers citing "Deciphering the Factors Influencing the Efficacy of Chain-of-Thought: Probability, Memorization, and Noisy Reasoning"

6 / 6 papers shown

Title
MathGAP: Out-of-Distribution Evaluation on Problems with Arbitrarily Complex Proofs Andreas Opedal Haruki Shirakami Bernhard Schölkopf Abulhair Saparov Mrinmaya Sachan LRM 54 1 0 17 Feb 2025
Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought Abulhair Saparov He He ELM LRM ReLM 116 270 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 87 64 0 26 Sep 2022
Text and Patterns: For Effective Chain of Thought, It Takes Two to Tango Aman Madaan Amir Yazdanbakhsh LRM 139 115 0 16 Sep 2022
Large Language Models are Zero-Shot Reasoners Takeshi Kojima S. Gu Machel Reid Yutaka Matsuo Yusuke Iwasawa ReLM LRM 291 4,048 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 297 3,163 0 21 Mar 2022