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.

翻译：链式思维提示方法因其能够揭示底层复杂推理过程而在各类自然语言处理任务中取得成功。此类推理过程通常呈现隐性结构化步骤，近期研究也开始探索如何捕获更显式结构化的推理流程。本文提出Tab-CoT这种新颖的表格格式链式思维提示方法，该方法能以高度结构化的方式显式建模复杂推理过程。尽管方法简单，我们证明了该方法能够跨维度执行推理（即同时进行行和列推理）。通过在多种推理任务上的广泛实验，我们展示了该方法强大的零样本与少样本学习能力。