@inproceedings{tyagi-etal-2025-aestar,
    title = "Aestar at {S}em{E}val-2025 Task 8: Agentic {LLM}s for Question Answering over Tabular Data",
    author = "Tyagi, Rishit  and
      Gupta, Mohit  and
      Bouri, Rahul",
    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-1.292/",
    pages = "2249--2255",
    ISBN = "979-8-89176-273-2",
    abstract = "Question Answering over Tabular Data (Table QA) presents unique challenges due to the diverse structure, size, and data types of real-world tables. The SemEval 2025 Task 8 (DataBench) introduced a benchmark composed of large-scale, domain-diverse datasets to evaluate the ability of models to accurately answer structured queries. We propose a Natural Language to SQL (NL-to-SQL) approach leveraging large language models (LLMs) such as GPT-4o, GPT-4o-mini, and DeepSeek v2:16b to generate SQL queries dynamically. Our system follows a multi-stage pipeline involving example selection, SQL query generation, answer extraction, verification, and iterative refinement. Experiments demonstrate the effectiveness of our approach, achieving 70.5{\%} accuracy on DataBench QA and 71.6{\%} on DataBench Lite QA, significantly surpassing baseline scores of 26{\%} and 27{\%} respectively. This paper details our methodology, experimental results, and alternative approaches, providing insights into the strengths and limitations of LLM-driven Table QA."
}