@inproceedings{wan-etal-2025-ucsc,
    title = "{UCSC} at {S}em{E}val-2025 Task 8: Question Answering over Tabular Data",
    author = "Wan, Neng  and
      Huang, Sicong  and
      Ubale, Esha  and
      Lane, Ian",
    editor = "Rosenthal, Sara  and
      Ros{\'a}, Aiala  and
      Ghosh, Debanjan  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2025-08/2025.semeval-1.266/",
    pages = "2050--2058",
    ISBN = "979-8-89176-273-2",
    abstract = "Table question answering (Table QA) remains challenging due to the varied structures of tables and the complexity of queries, which often require specialized reasoning. We introduce a system that leverages large language models (LLMs) to generate executable code as an intermediate step for answering questions on tabular data. The methodology uniformly represents tables as dataframes and prompts an LLM to translate natural-language questions into code that can be executed on these tables. This approach addresses key challenges by handling diverse table formats, enhancing interpretability through code execution. Experimental results on the DataBench benchmarks demonstrate that the proposed code-then-execute approach achieves high accuracy. Moreover, by offloading computation to code execution, the system requires fewer LLM invocations, thereby improving efficiency. These findings highlight the effectiveness of an LLM-based coding approach for reliable, scalable, and interpretable Table QA."
}