SQLucid: Grounding Natural Language Database Queries with Interactive Explanations

Though recent advances in machine learning have led to significant improvements in natural language interfaces for databases, the accuracy and reliability of these systems remain limited, especially in high-stakes domains. This paper introduces SQLucid, a novel user interface that bridges the gap between non-expert users and complex database querying processes. SQLucid addresses existing limitations by integrating visual correspondence, intermediate query results, and editable step-by-step SQL explanations in natural language to facilitate user understanding and engagement. This unique blend of features empowers users to understand and refine SQL queries easily and precisely. Two user studies and one quantitative experiment were conducted to validate SQLucid’s effectiveness, showing significant improvement in task completion accuracy and user confidence compared to existing interfaces. Our code is available at https://github.com/magic-YuanTian/SQLucid.

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Publication Details

Published
2024-10-11
DOI
https://doi.org/10.1145/3654777.3676368
Citations
7
Primary Topic
Semantic Web and Ontologies
Type
article
Field-Weighted Citation Impact
2.43
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article

SQLucid: Grounding Natural Language Database Queries with Interactive Explanations

Toby Jia-Jun Li, Yuan Tian, Jonathan K. Kummerfeld, Tianyi Zhang
7 citations
Semantic Web and Ontologies
2.43
article

SQLucid: Grounding Natural Language Database Queries with Interactive Explanations

Toby Jia-Jun Li, Yuan Tian, Jonathan K. Kummerfeld, Tianyi Zhang
article en
7 citations

Abstract

Though recent advances in machine learning have led to significant improvements in natural language interfaces for databases, the accuracy and reliability of these systems remain limited, especially in high-stakes domains. This paper introduces SQLucid, a novel user interface that bridges the gap between non-expert users and complex database querying processes. SQLucid addresses existing limitations by integrating visual correspondence, intermediate query results, and editable step-by-step SQL explanations in natural language to facilitate user understanding and engagement. This unique blend of features empowers users to understand and refine SQL queries easily and precisely. Two user studies and one quantitative experiment were conducted to validate SQLucid’s effectiveness, showing significant improvement in task completion accuracy and user confidence compared to existing interfaces. Our code is available at https://github.com/magic-YuanTian/SQLucid.

University of Notre Dame (US), The University of Sydney (AU), Purdue University West Lafayette (US)
Quality Education
Openalex Percentile: Top 9%
Semantic Web and Ontologies
2.43
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