Tab-CoT: Zero-shot Tabular Chain of Thought
This work addresses the need for more explicitly structured reasoning in NLP, offering a novel method that could enhance performance in reasoning tasks, though it appears incremental as it builds on existing CoT approaches.
The paper tackled the problem of modeling complex reasoning processes in NLP tasks by proposing Tab-CoT, a tabular-format chain-of-thought prompting method, and demonstrated its strong zero-shot and few-shot capabilities across various reasoning tasks.
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.