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：基于大型语言模型（LLMs）的表格推理是解决表格理解任务（如表格问答和事实验证）的一个有前景的方向。与通用推理相比，表格推理需要从自由形式的问题和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上下文形式纳入推理链，但如何有效利用表格数据于推理链中仍是一个未解决的问题。我们提出链式表格框架，其中表格数据被显式用作推理链中中间思想的代理。具体而言，我们通过上下文学习引导LLMs迭代生成操作并更新表格，以构建表格推理链。LLMs可基于先前操作的结果动态规划下一步操作。这种表格的持续演化形成一条链，展示了给定表格问题的推理过程。该链承载了中间结果的结构化信息，从而实现更准确可靠的预测。链式表格在多个LLM选择的WikiTQ、FeTaQA和TabFact基准测试中达到了新的最优性能。