General Table Question Answering via Answer-Formula Joint Generation
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915472006447104 |
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| author | Wang, Zhongyuan Zhang, Richong Nie, Zhijie Mao, Hangyu |
| author_facet | Wang, Zhongyuan Zhang, Richong Nie, Zhijie Mao, Hangyu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_12345 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | General Table Question Answering via Answer-Formula Joint Generation Wang, Zhongyuan Zhang, Richong Nie, Zhijie Mao, Hangyu Computation and Language Artificial Intelligence 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. |
| title | General Table Question Answering via Answer-Formula Joint Generation |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2503.12345 |