用公式统一生成答案与计算逻辑，提升表格问答通用性。
General Table Question Answering via Answer-Formula Joint Generation
- 以表格公式为可执行表示，统一处理多种表结构和问题类型
- 在WikiTableQuestion、HiTab、TabFact上达到新最好效果
- 仅用一个大模型同时输出答案和公式，适合复杂推理场景
现有表格问答方法多依赖大模型生成答案文本、SQL或代码，虽能解决复杂推理，但泛化能力有限。本文首次探索使用广泛且规范的电子表格公式作为可执行表示来解决不同结构表格的复杂推理问题。我们构建了首个包含公式标注的大规模表格问答数据集FormulaQA，基于现有数据集生成。同时提出通用表格问答框架TabAF，通过单一大模型骨干同时解码答案与公式。大量实验表明，相同模型规模下，TabAF在WikiTableQuestion、HiTab和TabFact三个基准上均达到当前最优性能，展现出强大泛化能力。
原文摘要 · Abstract (English)
Advanced table question answering (TableQA) methods prompt large language models (LLMs) to generate answer text, SQL query, Python code, or custom operation, which impressively improve the complex reasoning problems in the TableQA task. However, these methods lack the versatility to cope with specific question types or table structures. In contrast, the Spreadsheet Formula, the widely used and well-defined operation language for tabular data, has not been thoroughly explored to solve TableQA. In this paper, we first attempt to use the Formula as the executable representation for solving complex reasoning on tables with different structures. Specifically, we construct \texttt{FromulaQA}, a large Formula-annotated TableQA dataset from existing datasets. In addition, we propose \texttt{TabAF}, a general table answering framework to solve multiple types of tasks over multiple types of tables simultaneously, which decodes answers and Formulas with a single LLM backbone. Extensive experiments demonstrate the versatility and generalization of \texttt{TabAF}. Under the same model size, \texttt{TabAF} achieves new state-of-the-art performance on the WikiTableQuestion, HiTab, and TabFact.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。