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Automated Data Visualization from Natural Language via Large Language Models: An Exploratory Study

Summary: Empirical NL2Vis study showing LLMs can outperform prior deep models on unseen/multi-table tables, especially with schema-aware prompt serialization and few-shot in-context learning. Also probes failure modes and iterative refinement (CoT/role-play/code interpreter) to improve generated visualizations. (summarized by gpt-5.4-mini on May 24 2026)

Paper ID
7014
Venue
SIGMOD
Year
2024
Pagerank
5.4802202e-05
Overall Rank
8,140 | 44.16%
DOI
10.1145/3654992

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{wu_sigmod24,
        title = {{Automated Data Visualization from Natural Language via Large Language Models: An Exploratory Study}},
        author = {Wu, Yang and Wan, Yao and Zhang, Hongyu and Sui, Yulei and Wei, Wucai and Zhao, Wei and Xu, Guandong and Jin, Hai},
        series = {{SIGMOD} '24},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3654992},
        url = {https://dl.acm.org/doi/10.1145/3654992},
        year = {2024}
}

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