用人工构建的图谱提升多表问答的准确率
Plugging Schema Graph into Multi-Table QA: A Human-Guided Framework for Reducing LLM Reliance
- 用人类标注的关系图谱显式建模表间连接
- 在真实工业数据集上准确率提升23.5%
- 适合需要高可靠性的企业级数据查询场景
大型语言模型在单表问答中表现良好，但在多表问答中仍面临表间模式链接不可靠的问题。现有基于语义相似度的方法仅适用于简化手工数据集，在包含大量多样列的真实复杂场景下表现不佳。为此，我们提出一种基于图的框架，利用人工标注的关联知识显式编码表间链接与连接路径。针对自然语言查询，该方法在图上搜索并构建可解释的推理链，结合剪枝和子路径合并策略提升效率与连贯性。在标准基准和一个真实大规模数据集上的实验表明，该方法有效。据我们所知，这是首个应用于真正复杂工业表格数据的多表问答系统。
原文摘要 · Abstract (English)
Large language models (LLMs) have shown promise in table Question Answering (Table QA). However, extending these capabilities to multi-table QA remains challenging due to unreliable schema linking across complex tables. Existing methods based on semantic similarity work well only on simplified hand-crafted datasets and struggle to handle complex, real-world scenarios with numerous and diverse columns. To address this, we propose a graph-based framework that leverages human-curated relational knowledge to explicitly encode schema links and join paths. Given a natural language query, our method searches on graph to construct interpretable reasoning chains, aided by pruning and sub-path merging strategies to enhance efficiency and coherence. Experiments on both standard benchmarks and a realistic, large-scale dataset demonstrate the effectiveness of our approach. To our knowledge, this is the first multi-table QA system applied to truly complex industrial tabular data.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。