Plugging Schema Graph into Multi-Table QA: A Human-Guided Framework for Reducing LLM Reliance

• URL: http://arxiv.org/abs/2506.04427v1
• Date: Wed, 04 Jun 2025 20:21:52 GMT
• Title: Plugging Schema Graph into Multi-Table QA: A Human-Guided Framework for Reducing LLM Reliance
• Authors: Xixi Wang, Miguel Costa, Jordanka Kovaceva, Shuai Wang, Francisco C. Pereira,
• Abstract summary: We propose a graph-based framework that leverages human-curated relational knowledge to explicitly encode schema links and join paths.<n>Given a natural language query, our method searches this graph to construct interpretable reasoning chains, aided by pruning and sub-path merging strategies.<n>Experiments on both standard benchmarks and a realistic, large-scale dataset demonstrate the effectiveness of our approach.
• Score: 8.304761523814564
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 this 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.

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