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Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation

Summary: Systematic benchmark of prompt-engineering components (question representation, example selection/organization) and token-efficiency for LLM-based Text-to-SQL. Proposes DAIL-SQL (86.6% execution on Spider) and evaluates open-source LLMs with supervised fine-tuning, revealing accuracy/efficiency/cost trade-offs. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13550
Venue
VLDB
Year
2024
Pagerank
0.00022468369
Overall Rank
279 | 98.09%
DOI
10.14778/3641204.3641221

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Authors

BibTeX Citation

@article{gao_vldb24,
        title = {{Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation}},
        author = {Gao, Dawei and Wang, Haibin and Qian, Yichen and Li, Yaliang and Ding, Bolin and Sun, Xiuyu and Zhou, Jingren},
        journal = {PVLDB},
        series = {{VLDB} '24},
        volume = {17},
        number = {5},
        pages = {1132--1145},
        doi = {10.14778/3641204.3641221},
        url = {https://doi.org/10.14778/3641204.3641221},
        year = {2024}
}

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