2022-01-28
arXiv

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

Jason Wei (Google Research) , Xuezhi Wang (Google Research) , Dale Schuurmans (Google Research) , Maarten Bosma (Google Research) , Brian Ichter (Google Research)
The paper demonstrates that providing a chain of thought in prompts significantly enhances the reasoning capabilities of large language models, leading to improved performance on various reasoning tasks. This method, even with few exemplars, can achieve state-of-the-art accuracy, surpassing fine-tuned models.
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.