@inproceedings{zhou-etal-2025-ccnu,
    title = "{CCNU} at {S}em{E}val-2025 Task 8: Enhancing Question Answering on Tabular Data with Two-Stage Corrections",
    author = "Zhou, Chenlian  and
      Cai, Xilu  and
      Tong, Yajuan  and
      Wu, Chengzhao  and
      Xu, Xin  and
      Chen, Guanyi  and
      He, Tingting",
    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/transition-to-people-yaml/2025.semeval-1.115/",
    pages = "841--845",
    ISBN = "979-8-89176-273-2",
    abstract = "We present the system developed by the Central China Normal University (CCNU) team for the SemEval-2025 shared task 8, which focuses on Question-Answering (QA) for tabular data. Our approach leverages multiple Large Language Models (LLMs), conducting tabular QA as code completion. Additionally, to improve its reliability, we introduce a two-stage corrections mechanism, in which we instruct the LLM to correct the code according to the judges of whether the code is executable and whether the answer obtained from executing the code is semantically consistent with the question. The experiment demonstrates that code correction works but answer correction does not. Finally, we discuss other unsuccessful approaches explored during our development process."
}