Abstract
Table Question Answering (TableQA) enables natural language interaction with structured tabular data. However, existing large language model (LLM) approaches face critical limitations: context length constraints that restrict data handling capabilities, hallucination issues that compromise answer reliability, and single-agent architectures that struggle with complex reasoning scenarios involving semantic relationships and multi-hop logic. This paper introduces DataFactory, a multi-agent framework that addresses these limitations through specialized team coordination and automated knowledge transformation. The framework comprises a Data Leader employing the ReAct paradigm for reasoning orchestration, together with dedicated Database and Knowledge Graph teams, enabling the systematic decomposition of complex queries into structured and relational reasoning tasks. We formalize automated data-to-knowledge graph transformation via the mapping function T:D x S x R -> G, and implement natural language-based consultation that - unlike fixed workflow multi-agent systems - enables flexible inter-agent deliberation and adaptive planning to improve coordination robustness. We also apply context engineering strategies that integrate historical patterns and domain knowledge to reduce hallucinations and improve query accuracy. Across TabFact, WikiTableQuestions, and FeTaQA, using eight LLMs from five providers, results show consistent gains. Our approach improves accuracy by 20.2% (TabFact) and 23.9% (WikiTQ) over baselines, with significant effects (Cohen's d > 1). Team coordination also outperforms single-team variants (+5.5% TabFact, +14.4% WikiTQ, +17.1% FeTaQA ROUGE-2). The framework offers design guidelines for multi-agent collaboration and a practical platform for enterprise data analysis through integrated structured querying and graph-based knowledge representation.
Abstract (translated)
表格问答（TableQA）使用户能够以自然语言的形式与结构化的表格数据进行交互。然而，现有的大型语言模型（LLM）方法面临关键限制：上下文长度的约束限制了它们处理数据的能力；幻觉问题影响了答案的可靠性；以及单一代理架构在涉及语义关系和多步逻辑推理的复杂场景中表现不佳。本文介绍了DataFactory，这是一个通过专门团队协作和自动化知识转换来解决这些限制的多智能体框架。该框架包含一个使用ReAct范式的数据领导者用于推理协调，同时还包括专门的数据库和知识图谱小组，这使得将复杂的查询分解为结构化和关系推理任务成为可能。我们通过映射函数T:D x S x R -> G实现了自动化数据到知识图谱的转换，并实施了基于自然语言的咨询机制——不同于固定工作流多智能体系统，这种机制允许灵活的代理间讨论和适应性规划以增强协作稳健性。此外，我们应用上下文工程技术整合历史模式和领域知识，从而减少幻觉并提高查询准确性。 在TabFact、WikiTableQuestions和FeTaQA数据集上使用来自五家提供商的八种LLM进行实验显示了持续的改进：我们的方法比基线提高了20.2%（TabFact）和23.9%（WikiTQ），Cohen's d值大于1表明效果显著。团队协作也优于单个团队变体，分别在TabFact、WikiTableQuestions和FeTaQA ROUGE-2上提高5.5%、14.4%和17.1%。 该框架为多智能体合作提供了设计准则，并通过集成结构化查询和基于图的知识表示提供了一个实用的企业数据分析平台。
URL
https://arxiv.org/abs/2603.09152
