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TABLECOPILOT: A Table Assistant Empowered by Natural Language Conditional Table Discovery
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AI
DOI：10.14778/3750601.3750681.png)
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Abstract
En 中文
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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3.3
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