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.
@article{arxiv.2506.04427,
title = {Plugging Schema Graph into Multi-Table QA: A Human-Guided Framework for Reducing LLM Reliance},
author = {Xixi Wang and Miguel Costa and Jordanka Kovaceva and Shuai Wang and Francisco C. Pereira},
journal= {arXiv preprint arXiv:2506.04427},
year = {2025}
}