Table-based reasoning with large language models (LLMs) is a promising direction to tackle many table understanding tasks, such as table-based question answering and fact verification. Compared with generic reasoning, table-based reasoning requires the extraction of underlying semantics from both free-form questions and semi-structured tabular data. Chain-of-Thought and its similar approaches incorporate the reasoning chain in the form of textual context, but it is still an open question how to effectively leverage tabular data in the reasoning chain. We propose the Chain-of-Table framework, where tabular data is explicitly used in the reasoning chain as a proxy for intermediate thoughts. Specifically, we guide LLMs using in-context learning to iteratively generate operations and update the table to represent a tabular reasoning chain. LLMs can therefore dynamically plan the next operation based on the results of the previous ones. This continuous evolution of the table forms a chain, showing the reasoning process for a given tabular problem. The chain carries structured information of the intermediate results, enabling more accurate and reliable predictions. Chain-of-Table achieves new state-of-the-art performance on WikiTQ, FeTaQA, and TabFact benchmarks across multiple LLM choices.

翻译：基于大语言模型的表格推理是解决表格理解任务（如表格问答与事实验证）的前沿方向。与通用推理相比，表格推理需从自由形式问题与半结构化表格数据中提取潜在语义。思维链及其类似方法以文本语境形式构建推理链，但如何有效利用推理链中的表格数据仍是一个开放性问题。本文提出的链式表格框架将表格数据作为中间推理的显式代理：具体而言，通过上下文学习引导大语言模型迭代生成操作并更新表格，从而形成表格推理链。大语言模型能够根据先前操作结果动态规划后续操作，这种表格的持续演化构成推理链，完整呈现给定表格问题的推理过程。该推理链承载中间结果的结构化信息，可实现更精确可靠的预测。链式表格在WikiTQ、FeTaQA与TabFact基准测试中，针对多种大语言模型选择均取得了当前最优性能。