TableCopilot: A Table Assistant Empowered by Natural Language Conditional Table Discovery
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915382878535680 |
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| author | Cui, Lingxi Jiang, Guanyu Li, Huan Chen, Ke Shou, Lidan Chen, Gang |
| author_facet | Cui, Lingxi Jiang, Guanyu Li, Huan Chen, Ke Shou, Lidan Chen, Gang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_08283 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TableCopilot: A Table Assistant Empowered by Natural Language Conditional Table Discovery Cui, Lingxi Jiang, Guanyu Li, Huan Chen, Ke Shou, Lidan Chen, Gang Databases 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. |
| title | TableCopilot: A Table Assistant Empowered by Natural Language Conditional Table Discovery |
| topic | Databases |
| url | https://arxiv.org/abs/2507.08283 |