@inproceedings{lopez-gude-etal-2025-lys,
    title = "{L}y{S} at {S}em{E}val 2025 Task 8: Zero-Shot Code Generation for Tabular {QA}",
    author = "L{\'o}pez Gude, Adri{\'a}n  and
      Santos R{\'i}os, Roi  and
      Prado Vali{\~n}o, Francisco  and
      Ezquerro, Ana  and
      Vilares, Jes{\'u}s",
    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-1.171/",
    pages = "1282--1288",
    ISBN = "979-8-89176-273-2",
    abstract = "We developed a zero-shot pipeline that leverages an Large Language Model to generate functional code capable of extracting the relevant information from tabular data based on an input question. Our approach consists of a modular pipeline where the main code generator module is supported by additional components that identify the most relevant columns and analyze their data types to improve extraction accuracy. In the event that the generated code fails, an iterative refinement process is triggered, incorporating the error feedback into a new generation prompt to enhance robustness. Our results show that zero-shot code generation is a valid approach for Tabular QA, achieving rank 33 of 53 in the test phase despite the lack of task-specific fine-tuning."
}