@inproceedings{rahnamoun-shamsfard-2025-sbu,
    title = "{SBU}-{NLP} at {S}em{E}val-2025 Task 8: Self-Correction and Collaboration in {LLM}s for Tabular Question Answering",
    author = "Rahnamoun, Rashin  and
      Shamsfard, Mehrnoush",
    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-1.97/",
    pages = "703--711",
    ISBN = "979-8-89176-273-2",
    abstract = "This paper explains the submission of the SBU-NLP team at SemEval-2025 Task 8: question-answering over tabular data. We present a novel algorithm for this task, aimed at systems capable of interpreting large tables and providing accurate answers to natural language queries. The evaluation uses the DataBench dataset, which covers a wide range of topics and reflects the complexity of real-world tabular data. Our approach incorporates a self-correction mechanism that iteratively refines LLM-generated code to address errors and prevent common mistakes. Additionally, a multi-LLM collaborative strategy is employed to generate answers, where responses from multiple LLMs are compared, and the majority consensus or a valid alternative is selected. The method relies exclusively on open-source models, avoiding costly processes like training or fine-tuning. Experimental results demonstrate that combining multiple LLMs with self-correction leads to significant performance improvements. However, challenges arise with list-based answers and responses involving multiple numerical, string, or boolean values, where further refinement is needed. The proposed simple system was among the top performers in both Subtask A and Subtask B among open-source models in the competition."
}