  The chain-of-though (CoT) prompting methods were successful in various
natural language processing (NLP) tasks thanks to their ability to unveil the
underlying complex reasoning processes. Such reasoning processes typically
exhibit implicitly structured steps. Recent efforts also started investigating
methods to encourage more explicitly structured reasoning procedures to be
captured. In this work, we propose Tab-CoT, a novel tabular-format CoT
prompting method, which allows the complex reasoning process to be explicitly
modelled in a highly structured manner. Despite its simplicity, we show that
our approach is capable of performing reasoning across multiple dimensions
(i.e., both rows and columns). We demonstrate our approach's strong zero-shot
and few-shot capabilities through extensive experiments on a range of reasoning
tasks.
