@inproceedings{hu-etal-2025-ynu,
    title = "{YNU}-{HPCC} at {S}em{E}val-2025 Task 8: Enhancing Question-Answering over Tabular Data with {T}able{GPT}2",
    author = "Hu, Kaiwen  and
      Wang, Jin  and
      Zhang, Xuejie",
    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-1.297/",
    pages = "2276--2282",
    ISBN = "979-8-89176-273-2",
    abstract = "This paper describes our systems for SemEval 2025 Task8, Question Answering over Tabular Data. This task encourages us to develop a system that answers questions of the kind present in DataBench over day-to-day datasets, where the answer is either a number, a categorical value, a boolean value, or lists of several types. Participating in Task 8, we engage in all subtasks. The challenge lies in the multi-step reasoning process of converting natural language queries into executable code. This challenge is exacerbated by the limitations of current methods, such as chaining reasoning, which have difficulty handling complex multi-step reasoning paths due to difficulty evaluating intermediate steps. In the official ranking, we obtain a score of 65.64. On the final competition test set, our DataBench accuracy is 65.64{\%}, and DataBench Lite accuracy is 66.62{\%}. Both exceed the baseline (26{\%}). The competitive results in two subtasks demonstrate the effectiveness of our systems."
}