@inproceedings{tiwari-aryal-2025-howard,
    title = "{H}oward {U}niversity-{AI}4{PC} at {S}em{E}val-2025 Task 8: {D}eep{T}ab{C}oder - Code-based Retrieval and In-context Learning for Question-Answering over Tabular Data",
    author = "Tiwari, Saharsha  and
      Aryal, Saurav",
    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-1.223/",
    pages = "1702--1708",
    ISBN = "979-8-89176-273-2",
    abstract = "This paper presents our approach, named DeepTabCoder, to SemEval 2025 - Task 8: DataBench, which focuses on question-answering over tabular data. We utilize a code-based retrieval system combined with in-context learning, which generates and executes code to answer questions, leveraging DeepSeek-V3 for code generation. DeepTabCoder outperforms the baseline, achieving accuracies of 81.42{\%} on the DataBench dataset and 80.46{\%} on the DataBench Lite dataset."
}