Agentic LLMs for Question Answering over Tabular Data

• URL: http://arxiv.org/abs/2509.09234v1
• Date: Thu, 11 Sep 2025 08:12:38 GMT
• Title: Agentic LLMs for Question Answering over Tabular Data
• Authors: Rishit Tyagi, Mohit Gupta, Rahul Bouri,
• Abstract summary: Question Answering over Tabular Data (Table QA) presents unique challenges due to the diverse structure, size, and data types of real-world tables.<n>This paper details our methodology, experimental results, and alternative approaches, providing insights into the strengths and limitations of Table QA.
• Score: 6.310433217813068
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Question Answering over Tabular Data (Table QA) presents unique challenges due to the diverse structure, size, and data types of real-world tables. The SemEval 2025 Task 8 (DataBench) introduced a benchmark composed of large-scale, domain-diverse datasets to evaluate the ability of models to accurately answer structured queries. We propose a Natural Language to SQL (NL-to-SQL) approach leveraging large language models (LLMs) such as GPT-4o, GPT-4o-mini, and DeepSeek v2:16b to generate SQL queries dynamically. Our system follows a multi-stage pipeline involving example selection, SQL query generation, answer extraction, verification, and iterative refinement. Experiments demonstrate the effectiveness of our approach, achieving 70.5\% accuracy on DataBench QA and 71.6\% on DataBench Lite QA, significantly surpassing baseline scores of 26\% and 27\% respectively. This paper details our methodology, experimental results, and alternative approaches, providing insights into the strengths and limitations of LLM-driven Table QA.

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