@inproceedings{evangelatos-etal-2025-ails,
    title = "{AILS}-{NTUA} at {S}em{E}val-2025 Task 8: Language-to-Code prompting and Error Fixing for Tabular Question Answering",
    author = "Evangelatos, Andreas  and
      Filandrianos, George  and
      Lymperaiou, Maria  and
      Voulodimos, Athanasios  and
      Stamou, Giorgos",
    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/transition-to-people-yaml/2025.semeval-1.188/",
    pages = "1423--1435",
    ISBN = "979-8-89176-273-2",
    abstract = "In this paper, we present our submission to SemEval-2025 Task 8: Question Answering over Tabular Data. This task, evaluated on the DataBench dataset, assesses Large Language Models' (LLMs) ability to answer natural language questions over structured data while addressing topic diversity and table size limitations in previous benchmarks. We propose a system that employs effective LLM prompting to translate natural language queries into executable code, enabling accurate responses, error correction, and interpretability. Our approach ranks first in both subtasks of the competition in the proprietary model category, significantly outperforming the organizer{'}s baseline."
}