  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.
