表链：在表格理解推理链中演变的表格

摘要

基于大型语言模型（LLMs）的基于表格的推理是解决许多表格理解任务的一个有前途的方向，例如基于表格的问答和事实验证。与通用推理相比，基于表格的推理需要从自由形式问题和半结构化表格数据中提取潜在语义。Chain-of-Thought及其类似方法以文本上下文的形式整合推理链，但如何有效地利用表格数据在推理链中仍然是一个悬而未决的问题。我们提出Chain-of-Table框架，其中表格数据明确地在推理链中用作中间思想的代理。具体来说，我们引导LLMs使用上下文学习来迭代生成操作并更新表格，以表示表格推理链。因此，LLMs可以根据先前操作的结果动态规划下一个操作。表格的持续演变形成一个链条，展示了给定表格问题的推理过程。该链携带中间结果的结构化信息，实现更准确和可靠的预测。Chain-of-Table在WikiTQ、FeTaQA和TabFact基准上实现了新的最先进性能，跨多个LLM选择。

English

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.

表链：在表格理解推理链中演变的表格

Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding

摘要

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