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arXiv · 2507.08283

TableCopilot: A Table Assistant Empowered by Natural Language Conditional Table Discovery

Lingxi Cui·Guanyu Jiang·Huan Li·Ke Chen·Lidan Shou·Gang Chen
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Abstract

The rise of LLM has enabled natural language-based table assistants, but existing systems assume users already have a well-formed table, neglecting the challenge of table discovery in large-scale table pools. To address this, we introduce TableCopilot, an LLM-powered assistant for interactive, precise, and personalized table discovery and analysis. We define a novel scenario, nlcTD, where users provide both a natural language condition and a query table, enabling intuitive and flexible table discovery for users of all expertise levels. To handle this, we propose Crofuma, a cross-fusion-based approach that learns and aggregates single-modal and cross-modal matching scores. Experimental results show Crofuma outperforms SOTA single-input methods by at least 12% on NDCG@5. We also release an instructional video, codebase, datasets, and other resources on GitHub to encourage community contributions. TableCopilot sets a new standard for interactive table assistants, making advanced table discovery accessible and integrated.

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Lingxi Cui, Guanyu Jiang, Huan Li, Ke Chen, Lidan Shou, Gang Chen. 2025-07-11. TableCopilot: A Table Assistant Empowered by Natural Language Conditional Table Discovery. https://arxiv.org/abs/2507.08283

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