@inproceedings{gao-etal-2025-i2r,
    title = "{I}2{R}-{NLP} at {S}em{E}val-2025 Task 8: Question Answering on Tabular Data",
    author = "Gao, Yuze  and
      Chen, Bin  and
      Su, Jian",
    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-1.14/",
    pages = "90--101",
    ISBN = "979-8-89176-273-2",
    abstract = "We present a Large Language Model (LLM) based system for question answering (QA) over tabular data that leverages multi-turn prompting to automatically generate executable Pandas functions. Our framework decomposes the problem into three key steps: (1) Answer Type Identification, where the system identifies the expected format of the response (e.g., boolean, number, category); (2) Pandas Function Generation, which generates a corresponding Pandas function using table metadata and in-context examples, and (3) Error Correction and Regeneration, where iteratively refining the function based on error feedback from executions. Evaluations on the SemEval-2025 Task 8 Tabular QA benchmark (Grijalba et al., 2024) demonstrate that our multi-turn approach significantly outperforms single-turn prompting models in exact match accuracy by 7.3{\%}. The proposed system not only improves code generation robustness but also paves the way for enhanced and adaptability in table-QA reasoning tasks. Our implementation is available at https://github.com/Gyyz/Question{\_}Answering-over-Tabular-Data."
}