Latest Research Papers
2022-01-28
arXiv
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
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.