@inproceedings{bangar-etal-2025-exploration,
    title = "Exploration Lab {IITK} at {S}em{E}val-2025 Task 8: Multi-{LLM} Agent {QA} over Tabular Data",
    author = "Bangar, Aditya  and
      Kumar, Ankur  and
      Mishra, Shlok  and
      Modi, Ashutosh",
    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-1.280/",
    pages = "2165--2169",
    ISBN = "979-8-89176-273-2",
    abstract = "This paper presents our Multi-LLM Agentic System that helps solve the problem of tabular question answering as posed in the SemEval Task-8: Question Answering over Tabular Data. Our system incorporates an Agentic Workflow where we assign each agent a role along with the context from other agents to better help resolve the ambiguity. As the user poses their question along with the dataframe, we firstly try to infer the types of the columns from the dataframe and also the expected answer type given the question and the column types. Then, the planner agent gives out a plan that tells us about the steps that we have to take to get the answer. Each step is written such that it helps us write one line of Python code. Then, we call the coding agent, which attempts to write the code given the information from the previous agents. After that, we perform a debugging pass through a debugging agent, which tries to resolve the issue given the previous context and finally deliver the answer if the code runs error-free. Our system achieved 14th place on the overall open-source models track."
}