  Large language models (LLMs) have achieved impressive performance on various
reasoning tasks. To further improve the performance, we propose MultiTool-CoT,
a novel framework that leverages chain-of-thought (CoT) prompting to
incorporate multiple external tools, such as a calculator and a knowledge
retriever, during the reasoning process. We apply MultiTool-CoT to the Task 2
dataset of NumGLUE, which requires both numerical reasoning and domain-specific
knowledge. The experiments show that our method significantly outperforms
strong baselines and achieves state-of-the-art performance.
