mlsanityData to 7 Oct 2026

GenEdit: Compounding Operators and Continuous Improvement to Tackle Text-to-SQL in the Enterprise

arXiv 2503.21602 ↗First posted 27 Mar 2025cs.AIcited by 4

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Abstract (arXiv)

Recent advancements in Text-to-SQL, driven by large language models, are democratizing data access. Despite these advancements, enterprise deployments remain challenging due to the need to capture business-specific knowledge, handle complex queries, and meet expectations of continuous improvements. To address these issues, we designed and implemented GenEdit: our Text-to-SQL generation system that improves with user feedback. GenEdit builds and maintains a company-specific knowledge set, employs a pipeline of operators decomposing SQL generation, and uses feedback to update its knowledge set to improve future SQL generations. We describe GenEdit's architecture made of two core modules: (i) decomposed SQL generation; and (ii) knowledge set edits based on user feedback. For generation, GenEdit leverages compounding operators to improve knowledge retrieval and to create a plan as chain-of-thought steps that guides generation. GenEdit first retrieves relevant examples in an initial retrieval stage where original SQL queries are decomposed into sub-statements, clauses or sub-queries. It then also retrieves instructions and schema elements. Using the retrieved contextual information, GenEdit then generates step-by-step plan in natural language on how to produce the query. Finally, GenEdit uses the plan to generate SQL, minimizing the need for model reasoning, which enhances complex SQL generation. If necessary, GenEdit regenerates the query based on syntactic and semantic errors. The knowledge set edits are recommended through an interactive copilot, allowing users to iterate on their feedback and to regenerate SQL queries as needed. Each generation uses staged edits which update the generation prompt. Once the feedback is submitted, it gets merged after passing regression testing and obtaining an approval, improving future generations.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 23 of its 31 references that are in the map. First posted on arXiv 27 Mar 2025.

Builds on

23 of 31 references.

References · 23

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  2. 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
  3. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,120
  4. 2017Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learningcited by 679
  5. 2023Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLscited by 667
  6. 2023Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Modelscited by 550
  7. 2023DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correctioncited by 465
  8. 2023Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluationcited by 378
  9. 2024CodeS: Towards Building Open-source Language Models for Text-to-SQLcited by 183
  10. 2024CHESS: Contextual Harnessing for Efficient SQL Synthesiscited by 164
  11. 2023C3: Zero-shot Text-to-SQL with ChatGPTcited by 159
  12. 2023MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQLcited by 158
11 more
  1. 2017SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learningcited by 135
  2. 2024Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQLcited by 118
  3. 2024MCS-SQL: Leveraging Multiple Prompts and Multiple-Choice Selection For Text-to-SQL Generationcited by 103
  4. 2024The Death of Schema Linking? Text-to-SQL in the Age of Well-Reasoned Language Modelscited by 67
  5. 2023ACT-SQL: In-Context Learning for Text-to-SQL with Automatically-Generated Chain-of-Thoughtcited by 65
  6. 2018Robust Text-to-SQL Generation with Execution-Guided Decodingcited by 51
  7. 2024Before Generation, Align it! A Novel and Effective Strategy for Mitigating Hallucinations in Text-to-SQL Generationcited by 49
  8. 2024BEAVER: An Enterprise Benchmark for Text-to-SQLcited by 37
  9. 2023Interactive Text-to-SQL Generation via Editable Step-by-Step Explanationscited by 19
  10. 2023Revisiting Prompt Engineering via Declarative Crowdsourcingcited by 12
  11. 2024End-to-end Text-to-SQL Generation within an Analytics Insight Enginecited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2025BenchPress: A Human-in-the-Loop Annotation System for Rapid Text-to-SQL Benchmark Curationcited by 3
  2. 2026Fine-Grained Table Retrieval Through the Lens of Complex Queriescited by 1
  3. 2026SQLMorph: Query Mutation and Fine-Grained Metrics for Text-to-SQL Evaluationcited by 1
  4. 2026Exploring the Semantic Gap in Agentic Data Systems: A Formative Study of Operationalization Failures in Analytical Workflowscited by 0

Similar, not cited

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  1. 2025ORANGE: An Online Reflection ANd GEneration framework with Domain Knowledge for Text-to-SQL
  2. 2025Datrics Text2SQL: A Framework for Natural Language to SQL Query Generation
  3. 2025Text-to-SQL as Dual-State Reasoning: Integrating Adaptive Context and Progressive Generation
  4. 2026Beyond the Harness: End-to-End Optimization of Context Artifacts for Enterprise Text-to-SQL
  5. 2025BAPPA: Benchmarking Agents, Plans, and Pipelines for Automated Text-to-SQL Generation
  6. 2026EvolSQL: Structure-Aware Evolution for Scalable Text-to-SQL Data Synthesis
  7. 2026CSR-RAG: An Efficient Retrieval System for Text-to-SQL on the Enterprise Scale
  8. 2026Iteration Without Elaboration: A Simple ReAct Architecture Suffices for Text-to-SQL Generation