Self-Consistency Improves Chain of Thought Reasoning in Language Models

Abstract

Samples several independent reasoning paths for the same question and takes the most common answer, instead of trusting a single greedy chain. The intuition is that a correct derivation can be reached many ways while a wrong one is usually reached once.

@inproceedings{wang2022selfconsistency,
  author    = {Xuezhi Wang and Jason Wei and Dale Schuurmans and Quoc Le and Ed Chi and Sharan Narang and Aakanksha Chowdhery and Denny Zhou},
  title     = {Self-Consistency Improves Chain of Thought Reasoning in Language Models},
  booktitle = {International Conference on Learning Representations (ICLR) 2023},
  year      = {2022},
  eprint    = {2203.11171},
  archivePrefix = {arXiv},
  url       = {https://arxiv.org/abs/2203.11171}
}