Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Abstract

Shows that asking a model to write out intermediate reasoning steps before answering substantially improves multi-step arithmetic, commonsense and symbolic reasoning, and that the behaviour appears without any fine-tuning at sufficient model scale.

@inproceedings{wei2022chainofthought,
  author    = {Jason Wei and Xuezhi Wang and Dale Schuurmans and Maarten Bosma and Brian Ichter and Fei Xia and Ed Chi and Quoc Le and Denny Zhou},
  title     = {Chain-of-Thought Prompting Elicits Reasoning in Large Language Models},
  booktitle = {Advances in Neural Information Processing Systems 35 (NeurIPS 2022)},
  year      = {2022},
  eprint    = {2201.11903},
  archivePrefix = {arXiv},
  url       = {https://arxiv.org/abs/2201.11903}
}