What question did this study set out to answer?

The aim is to improve execution accuracy of small open-source models for text-to-SQL tasks by decomposing the problem into simpler components.

January 1, 2024Open Access

DTS-SQL: Decomposed Text-to-SQL with Small Large Language Models

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Randomized trial demonstrates improved execution accuracy in open-source models, highlighting potential for broader application.

The aim is to improve execution accuracy of small open-source models for text-to-SQL tasks by decomposing the problem into simpler components.
Proposed a two-stage fine-tuning approach for text-to-SQL tasks.
Evaluated the method on three large cross-domain datasets and two small language models.
Assessed the performance against proprietary large language models.
Achieved 60.31% execution accuracy on the BIRD hold-out test set with a 7B parameter model.
Improved execution accuracy by 3 to 7 percent compared to previous methods.
Aligned performance of open-source models closer to proprietary counterparts.

Pourreza et al. (2024) studied this question.

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January 1, 2024Open Access

View Full Paper

Ask AI

Bookmark

Authors

Randomized trial demonstrates improved execution accuracy in open-source models, highlighting potential for broader application.

The aim is to improve execution accuracy of small open-source models for text-to-SQL tasks by decomposing the problem into simpler components.
Proposed a two-stage fine-tuning approach for text-to-SQL tasks.
Evaluated the method on three large cross-domain datasets and two small language models.
Assessed the performance against proprietary large language models.
Achieved 60.31% execution accuracy on the BIRD hold-out test set with a 7B parameter model.
Improved execution accuracy by 3 to 7 percent compared to previous methods.
Aligned performance of open-source models closer to proprietary counterparts.

Pourreza et al. (2024) studied this question.

View Full Paper

Ask AI

Bookmark