  Program of Thoughts (PoT) is an approach characterized by its executable
intermediate steps, which ensure the accuracy of the logical calculations in
the reasoning process. Currently, PoT primarily uses Python. However, relying
solely on a single language may result in suboptimal solutions and overlook the
potential benefits of other programming languages. In this paper, we conduct
comprehensive experiments on the programming languages used in PoT and find
that no single language consistently delivers optimal performance across all
tasks and models. The effectiveness of each language varies depending on the
specific scenarios. Inspired by this, we propose a task and model agnostic
approach called MultiPoT, which harnesses strength and diversity from various
languages. Experimental results reveal that it significantly outperforms Python
Self-Consistency. Furthermore, it achieves comparable or superior performance
compared to the best monolingual PoT in almost all tasks across all models. In
particular, MultiPoT achieves more than 4.6% improvement on average on ChatGPT
(gpt-3.5-turbo-0701).
