mlsanityData to 7 Oct 2026

SQLucid: Grounding Natural Language Database Queries with Interactive Explanations

arXiv 2409.06178 ↗First posted 10 Sep 2024cs.HC cs.CLcited by 10

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Abstract (arXiv)

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.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 31 of its 94 references that are in the map. First posted on arXiv 10 Sep 2024.

Builds on

31 of 94 references.

References · 31

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  7. 2020Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performancecited by 396
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19 more
  1. 2019A Comprehensive Exploration on WikiSQL with Table-Aware Word Contextualizationcited by 88
  2. 2019Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questionscited by 80
  3. 2023"What It Wants Me To Say": Bridging the Abstraction Gap Between End-User Programmers and Code-Generating Large Language Modelscited by 48
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  7. 2017Rationalization: A Neural Machine Translation Approach to Generating Natural Language Explanationscited by 37
  8. 2022Is a Question Decomposition Unit All We Need?cited by 37
  9. 2023LLMs as Factual Reasoners: Insights from Existing Benchmarks and Beyondcited by 37
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  12. 2019Generating Natural Language Explanations for Visual Question Answering using Scene Graphs and Visual Attentioncited by 26
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  16. 2019Bridging the Semantic Gap with SQL Query Logs in Natural Language Interfaces to Databasescited by 15
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 5; 2026: 4.

10 papers.

  1. 2025Human-AI Experience in Integrated Development Environments: A Systematic Literature Reviewcited by 5
  2. 2026PV-SQL: Synergizing Database Probing and Rule-based Verification for Text-to-SQL Agentscited by 5
  3. 2025Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunitiescited by 4
  4. 2025M3: Conversational LLMs Simplify Secure Clinical Data Access, Understanding, and Analysiscited by 3
  5. 2025NeuroSync: Intent-Aware Code-Based Problem Solving via Direct LLM Understanding Modificationcited by 3
  6. 2025Interactive Program Synthesis for Modeling Collaborative Physical Activities from Narrated Demonstrationscited by 2
  7. 2026TACO: Task-Aware Column Description Generation Using LLMscited by 1
  8. 2024The Role of Accuracy and Validation Effectiveness in Conversational Business Analyticscited by 0
  9. 2026Bridging Natural Language and Interactive What-If Interfaces via LLM-Generated Declarative Specificationscited by 0
  10. 2026ISEE: Interactive Semantic Enrichment for Database Fieldscited by 0