@inproceedings{bergmann-lisboa-etal-2025-qleveranswering,
    title = "{Q}lever{A}nswering-{PUCRS} at {S}em{E}val-2025 Task 8: Exploring {LLM} agents, code generation and correction for Table Question Answering",
    author = "Bergmann Lisboa, Andr{\'e}  and
      Cardoso Azevedo, Lucas  and
      Costella Pessutto, Lucas Rafael",
    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-1.179/",
    pages = "1342--1350",
    ISBN = "979-8-89176-273-2",
    abstract = "Table Question Answering (TQA) is a challenging task that requires reasoning over structured data to extract accurate answers. This paper presents QleverAnswering-PUCRS, our submission to SemEval-2025 Task 8: DataBench, Question-Answering over Tabular Data. QleverAnswering-PUCRS is a modular multi-agent system that employs a structured approach to TQA. The approach revolves around breaking down the task into specialized agents, each dedicated to handling a specific aspect of the problem. Our system was evaluated on benchmark datasets and achieved competitive results, ranking mid-to-top positions in the SemEval-2025 competition. Despite these promising results, we identify areas for improvement, particularly in handling complex queries and nested data structures."
}