@inproceedings{bansal-etal-2025-tablewise,
    title = "{T}able{W}ise at {S}em{E}val-2025 Task 8: {LLM} Agents for {T}ab{QA}",
    author = "Bansal, Harsh  and
      Raj, Aman  and
      Sharma, Akshit  and
      Krishnamurthy, Parameswari",
    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-1.87/",
    pages = "623--626",
    ISBN = "979-8-89176-273-2",
    abstract = "Tabular Question Answering (TabQA) is a challenging task that requires models to comprehend structured tabular data and generate accurate responses based on complex reasoning. In this paper, we present our approach to SemEval Task 8: Tabular Question Answering, where we develop a large language model (LLM)-based agent capable of understanding and reasoning over tabular inputs. Our agent leverages a hybrid retrieval and generation strategy, incorporating structured table parsing, semantic understanding, and reasoning mechanisms to enhance response accuracy. We fine-tune a pre-trained LLM on domain-specific tabular data, integrating chain-of-thought prompting and adaptive decoding to improve multi-hop reasoning over tables. Experimental results demonstrate that our approach achieves competitive performance, effectively handling numerical operations, entity linking, and logical inference. Our findings suggest that LLM-based agents, when properly adapted, can significantly advance the state of the art in tabular question answering."
}