Tab-CoT：零样本表格化思维链
Tab-CoT: Zero-shot Tabular Chain of Thought
AI总结：
本文提出 Tab-CoT 表格化思维链提示方法，以高度结构化方式显式建模复杂推理过程，并通过多类推理任务实验验证其零样本与少样本能力。
摘要：
思维链（CoT）提示方法因其能够揭示底层的复杂推理过程，在多种自然语言处理（NLP）任务中取得了成功。这类推理过程通常表现出隐式结构化的步骤。近期的研究也开始探索如何鼓励捕获更显式结构化的推理过程。在本文中，我们提出 Tab-CoT，一种新颖的表格格式 CoT 提示方法，它允许以高度结构化的方式对复杂推理过程进行显式建模。尽管方法简单，我们表明该方法能够在多个维度（即行和列）上执行推理。我们通过在多种推理任务上的大量实验，证明了该方法强大的零样本和少样本能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
