Title: Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Authors: Jason Wei, Xuezhi Wang, Dale Schuurmans et al.
Year: 2022
arXiv: 2201.11903 (https://arxiv.org/abs/2201.11903)

Abstract:
We explore how generating a chain of thought -- a series of intermediate reasoning steps
-- significantly improves the ability of large language models to perform complex
reasoning. In particular, we show how such reasoning abilities emerge naturally in
sufficiently large language models via a simple method called chain of thought prompting,
where a few chain of thought demonstrations are provided as exemplars in prompting.
Experiments on three large language models show that chain of thought prompting improves
performance on a range of arithmetic, commonsense, and symbolic reasoning tasks. The
empirical gains can be striking. For instance, prompting a 540B-parameter language model
with just eight chain of thought exemplars achieves state of the art accuracy on the GSM8K
benchmark of math word problems, surpassing even finetuned GPT-3 with a verifier.
