UNITQA: A Unified Automated Tabular Question Answering System with Multi-Agent Large Language Models
Jun-Peng Zhu, Peng Cai, Kai Xu, Li Li, Yishen Sun, Shuai Zhou, Haihuang Su, Liu Tang, Qi Liu · 2025
Automated tabular question answering (TQA) has attracted significant attention in data analysis and natural language processing communities due to its powerful capabilities. The emergence of large language models (LLMs) has initiated a paradigm shift in this field. However, existing state-of-the-art approaches cannot generally operate on multiple tables from multiple heterogeneous systems, and the answer accuracy is insufficient to meet the demands of the industrial field. This paper presents UNITQA, a unified automated tabular question-answering system through multi-agent LLMs. First, UNITQA offers a user-friendly GUI interface that enables users to use natural language questions to execute TQA tasks. Second, UNITQA consists of five agents who collaborate to complete user-specified tasks. To efficiently orchestrate different agents, UNITQA utilizes a dynamic agent scheduling algorithm based on a finite-state machine. Third, UNITQA integrates a series of data connectors that allow UNITQA to access various tables from multiple heterogeneous systems. We have implemented and deployed UNITQA in numerous production environments and have demonstrated its usability and efficiency in representative real-world scenarios.