@inproceedings{hormazabal-lagos-etal-2025-mrt,
    title = "{MRT} at {S}em{E}val-2025 Task 8: Maximizing Recovery from Tables with Multiple Steps",
    author = "Hormaz{\'a}bal Lagos, Maximiliano  and
      Bueno S{\'a}ez, {\'A}lvaro  and
      Cerezo - Costas, H{\'e}ctor  and
      Alonso Doval, Pedro  and
      Alcalde Vesteiro, Jorge",
    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-1.68/",
    pages = "487--493",
    ISBN = "979-8-89176-273-2",
    abstract = "In this paper we expose our approach to solve the SemEval 2025 Task 8: Question-Answering over Tabular Data challenge. Our strategy leverages Python code generation with LLMs to interact with the table and get the answer to the questions. The process is composed of multiple steps: understanding the content of the table, generating natural language instructions in the form of steps to follow in order to get the answer, translating these instructions to code, running it and handling potential errors or exceptions. These steps use open source LLMs and fine grained optimized prompts for each task (step). With this approach, we achieved a score of 70.50{\%} for subtask 1."
}