mlsanityData to 7 Oct 2026

Interactive Text-to-SQL Generation via Editable Step-by-Step Explanations

arXiv 2305.07372 ↗First posted 12 May 2023cs.DB cs.CLcited by 19

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Abstract (arXiv)

Relational databases play an important role in business, science, and more. However, many users cannot fully unleash the analytical power of relational databases, because they are not familiar with database languages such as SQL. Many techniques have been proposed to automatically generate SQL from natural language, but they suffer from two issues: (1) they still make many mistakes, particularly for complex queries, and (2) they do not provide a flexible way for non-expert users to validate and refine incorrect queries. To address these issues, we introduce a new interaction mechanism that allows users to directly edit a step-by-step explanation of a query to fix errors. Our experiments on multiple datasets, as well as a user study with 24 participants, demonstrate that our approach can achieve better performance than multiple SOTA approaches. Our code and datasets are available at https://github.com/magic-YuanTian/STEPS.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 24 of its 51 references that are in the map. First posted on arXiv 12 May 2023.

Builds on

24 of 51 references.

References · 24

  1. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,120
  2. 2017Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learningcited by 679
  3. 2019RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parserscited by 420
  4. 2021PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Modelscited by 402
  5. 2023RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQLcited by 191
  6. 2019CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databasescited by 180
  7. 2020GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsingcited by 171
  8. 2019SParC: Cross-Domain Semantic Parsing in Contextcited by 141
  9. 2021Towards Robustness of Text-to-SQL Models against Synonym Substitutioncited by 110
  10. 2017Learning a Neural Semantic Parser from User Feedbackcited by 98
  11. 2020SmBoP: Semi-autoregressive Bottom-up Semantic Parsingcited by 97
  12. 2023Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL Parsingcited by 93
12 more
  1. 2021Exploring Underexplored Limitations of Cross-Domain Text-to-SQL Generalizationcited by 92
  2. 2019A Comprehensive Exploration on WikiSQL with Table-Aware Word Contextualizationcited by 88
  3. 2019Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questionscited by 80
  4. 2020Speak to your Parser: Interactive Text-to-SQL with Natural Language Feedbackcited by 44
  5. 2019Model-based Interactive Semantic Parsing: A Unified Framework and A Text-to-SQL Case Studycited by 40
  6. 2021NL-EDIT: Correcting semantic parse errors through natural language interactioncited by 33
  7. 2020IGSQL: Database Schema Interaction Graph Based Neural Model for Context-Dependent Text-to-SQL Generationcited by 29
  8. 2022Importance of Synthesizing High-quality Data for Text-to-SQL Parsingcited by 27
  9. 2020Tracking Interaction States for Multi-Turn Text-to-SQL Semantic Parsingcited by 18
  10. 2021Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic Parsingcited by 15
  11. 2020"What Do You Mean by That?" A Parser-Independent Interactive Approach for Enhancing Text-to-SQLcited by 11
  12. 2021Towards Transparent Interactive Semantic Parsing via Step-by-Step Correctioncited by 9

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Citing papers per year: 2023: 1; 2024: 6; 2025: 7; 2026: 5.

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  1. 2024Improving Steering and Verification in AI-Assisted Data Analysis with Interactive Task Decompositioncited by 29
  2. 2024Selective Prompt Anchoring for Code Generationcited by 16
  3. 2024Position: Towards Bidirectional Human-AI Alignmentcited by 15
  4. 2023Retrieval-augmented GPT-3.5-based Text-to-SQL Framework with Sample-aware Prompting and Dynamic Revision Chaincited by 12
  5. 2024SQLucid: Grounding Natural Language Database Queries with Interactive Explanationscited by 10
  6. 2025Can LLMs Narrate Tabular Data? An Evaluation Framework for Natural Language Representations of Text-to-SQL System Outputscited by 7
  7. 2025Human-AI Experience in Integrated Development Environments: A Systematic Literature Reviewcited by 5
  8. 2026PV-SQL: Synergizing Database Probing and Rule-based Verification for Text-to-SQL Agentscited by 5
  9. 2025GenEdit: Compounding Operators and Continuous Improvement to Tackle Text-to-SQL in the Enterprisecited by 4
  10. 2025DCG-SQL: Enhancing In-Context Learning for Text-to-SQL with Deep Contextual Schema Link Graphcited by 4
  11. 2026Learning to Retrieve: Dual-Level Long-Term Memory for Text-to-SQL Agentscited by 4
  12. 2025Exploring Database Normalization Effects on SQL Generationcited by 2
7 more
  1. 2024PKG API: A Tool for Personal Knowledge Graph Managementcited by 1
  2. 2024Understanding the Effect of Algorithm Transparency of Model Explanations in Text-to-SQL Semantic Parsingcited by 1
  3. 2025GBV-SQL: Guided Generation and SQL2Text Back-Translation Validation for Multi-Agent Text2SQLcited by 1
  4. 2025Continual Learning of Domain Knowledge from Human Feedback in Text-to-SQLcited by 1
  5. 2026EGREFINE: An Execution-Grounded Optimization Framework for Text-to-SQL Schema Refinementcited by 1
  6. 2026TACO: Task-Aware Column Description Generation Using LLMscited by 1
  7. 2026ISEE: Interactive Semantic Enrichment for Database Fieldscited by 0