@inproceedings{mokhtar-etal-2025-alexnlp,
    title = "{A}lex{NLP}-{MO} at {S}em{E}val-2025 Task 8: A Chain of Thought Framework for Question-Answering over Tabular Data",
    author = "Mokhtar, Omar  and
      Ghanem, Minah  and
      El - Makky, Nagwa",
    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-1.118/",
    pages = "865--873",
    ISBN = "979-8-89176-273-2",
    abstract = "Table Question Answering (TQA) involves extracting answers from structured data using natural language queries, a challenging task due to diverse table formats and complex reasoning. This work develops a TQA system using the DataBench dataset, leveraging large language models (LLMs) to generate Python code in a zero-shot manner. Our approach is highly generic, relying on a structured Chain-of-Thought framework to improve reasoning and data interpretation. Experimental results demonstrate that our method achieves high accuracy and efficiency, making it a flexible and effective solution for real-world tabular question answering."
}