We explore how generating achain 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 calledchain-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 PaLM 540B 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.