@inproceedings{antropova-etal-2025-tabaqa,
    title = "{T}aba{QA} at {S}em{E}val-2025 Task 8: Column Augmented Generation for Question Answering over Tabular Data",
    author = "Antropova, Ekaterina  and
      Kratkov, Egor  and
      Derunets, Roman  and
      Trofimova, Margarita  and
      Bondarenko, Ivan  and
      Panchenko, Alexander  and
      Konovalov, Vasily  and
      Savkin, Maksim",
    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-1.126/",
    pages = "937--952",
    ISBN = "979-8-89176-273-2",
    abstract = "The DataBench shared task in the SemEval-2025 competition aims to tackle the problem of QA from data in tables. Given the diversity of the structure of tables, there are different approaches to retrieving the answer. Although Retrieval-Augmented Generation (RAG) is a viable solution, extracting relevant information from tables remains challenging. In addition, the table can be prohibitively large for direct integration into the LLM context. In this paper, we address QA over tabular data first by identifying relevant columns that might contain the answers, then the LLM generates answers by providing the context of the relevant columns, and finally, the LLM refines its answers. This approach secured us 7th place in the DataBench lite category."
}