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.

翻译：表格问答（TableQA）实现了与结构化表格数据的自然语言交互。然而，现有的大语言模型（LLM）方法面临关键限制：上下文长度限制约束了数据处理能力，幻觉问题损害了答案可靠性，以及单智能体架构难以处理涉及语义关系和多跳逻辑的复杂推理场景。本文提出DataFactory，一个通过专业化团队协作与自动化知识转换来解决这些限制的多智能体框架。该框架包含采用ReAct范式进行推理编排的数据领导者（Data Leader），以及专门的数据库和知识图谱团队，能够将复杂查询系统性地分解为结构化与关系型推理任务。我们通过映射函数 T:D x S x R -> G 形式化地定义了从数据到知识图谱的自动化转换，并实现了基于自然语言的协商机制——与固定工作流的多智能体系统不同——该机制支持灵活的智能体间审议与自适应规划，从而提升协作的鲁棒性。我们还应用了整合历史模式与领域知识的上下文工程策略，以减少幻觉并提高查询准确性。在TabFact、WikiTableQuestions和FeTaQA三个数据集上，使用来自五个提供商的八种LLM进行实验，结果均显示出一致的性能提升。我们的方法在基线模型基础上将准确率提高了20.2%（TabFact）和23.9%（WikiTQ），且效应量显著（Cohen's d > 1）。团队协作机制也显著优于单团队变体（TabFact +5.5%，WikiTQ +14.4%，FeTaQA ROUGE-2 +17.1%）。该框架为多智能体协作提供了设计准则，并通过集成的结构化查询与基于图的知识表示，为企业数据分析提供了一个实用平台。