@inproceedings{hossein-zadeh-etal-2025-iust,
    title = "{IUST}{\_}{C}hamps at {S}em{E}val-2025 Task 8: Structured Prompting and Retry Policy for Tabular Question Answering",
    author = "Hossein Zadeh, Arshia  and
      Mayahinia, Aysa  and
      Ahmadi, Nafiseh",
    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/ingest-emnlp/2025.semeval-1.260/",
    pages = "2008--2013",
    ISBN = "979-8-89176-273-2",
    abstract = "This paper presents a novel approach to Question Answering over Tabular Data, as part of SemEval-2025 Task 8. Our system generates executable Python code to derive answers directly from structured data, leveraging open-source large language models. Key innovations include structured prompting, semantic column filtering, and a one-time retry mechanism to enhance accuracy and robustness. We evaluate our approach on the DataBench and DataBench{\_}Lite datasets, significantly outperforming the baseline accuracy (26-27{\%}) with our best system achieving 70.49{\%} accuracy on the test set. Ablation studies confirm that few-shot prompting and rule-based type classification are crucial for improved performance. Despite these advancements, challenges remain in handling complex table structures and ambiguous queries. Our findings highlight the effectiveness of code-generation based methods for tabular question answering and provide insights for further research in this area."
}