表格鏈：在推理鏈中演進的表格理解

摘要

基於大型語言模型（LLMs）的基於表格的推理是應對許多表格理解任務的一個有前途的方向，例如基於表格的問答和事實驗證。與通用推理相比，基於表格的推理需要從自由形式問題和半結構化表格數據中提取潛在語義。Chain-of-Thought及其類似方法將推理鏈以文本上下文的形式納入，但如何有效地利用表格數據在推理鏈中仍然是一個懸而未決的問題。我們提出Chain-of-Table框架，其中表格數據明確地在推理鏈中作為中間思維的代理使用。具體來說，我們引導LLMs使用上下文學習來迭代生成操作並更新表格以表示表格推理鏈。因此，LLMs可以根據先前操作的結果動態規劃下一個操作。表格的持續演變形成一個鏈，展示了給定表格問題的推理過程。該鏈攜帶中間結果的結構化信息，從而實現更準確和可靠的預測。Chain-of-Table在WikiTQ、FeTaQA和TabFact基準上實現了新的最先進性能，跨多種LLM選擇。

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.

表格鏈：在推理鏈中演進的表格理解

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

摘要

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