mlsanityData to 7 Oct 2026

Text-to-SQL Error Correction with Language Models of Code

arXiv 2305.13073 ↗First posted 22 May 2023cs.CL cs.AI cs.DBcited by 21

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Abstract (arXiv)

Despite recent progress in text-to-SQL parsing, current semantic parsers are still not accurate enough for practical use. In this paper, we investigate how to build automatic text-to-SQL error correction models. Noticing that token-level edits are out of context and sometimes ambiguous, we propose building clause-level edit models instead. Besides, while most language models of code are not specifically pre-trained for SQL, they know common data structures and their operations in programming languages such as Python. Thus, we propose a novel representation for SQL queries and their edits that adheres more closely to the pre-training corpora of language models of code. Our error correction model improves the exact set match accuracy of different parsers by 2.4-6.5 and obtains up to 4.3 point absolute improvement over two strong baselines. Our code and data are available at https://github.com/OSU-NLP-Group/Auto-SQL-Correction.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 36 of its 56 references that are in the map. First posted on arXiv 22 May 2023.

Builds on

36 of 56 references.

References · 36

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24 more
  1. 2019RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parserscited by 420
  2. 2021PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Modelscited by 402
  3. 2021Unified Pre-training for Program Understanding and Generationcited by 361
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  11. 2021Towards Robustness of Text-to-SQL Models against Synonym Substitutioncited by 110
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 4; 2025: 10; 2026: 4.

21 papers.

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  1. 2023Exploring Chain-of-Thought Style Prompting for Text-to-SQLcited by 69
  2. 2024A Survey of Text-to-SQL in the Era of LLMs: Where are we, and where are we going?cited by 54
  3. 2024When is Tree Search Useful for LLM Planning? It Depends on the Discriminatorcited by 46
  4. 2025Understanding, Detecting, and Repairing Real-World In-Context-Learning-Based Text-to-SQL Errorscited by 14
  5. 2024CoE-SQL: In-Context Learning for Multi-Turn Text-to-SQL with Chain-of-Editionscited by 13
  6. 2023Error Detection for Text-to-SQL Semantic Parsingcited by 11
  7. 2025SQLens: An End-to-End Framework for Error Detection and Correction in Text-to-SQLcited by 10
  8. 2025AmbiSQL: Interactive Ambiguity Detection and Resolution for Text-to-SQLcited by 10
  9. 2025Can LLMs Narrate Tabular Data? An Evaluation Framework for Natural Language Representations of Text-to-SQL System Outputscited by 7
  10. 2024DAC: Decomposed Automation Correction for Text-to-SQLcited by 6
  11. 2025Automatic database description generation for Text-to-SQLcited by 5
  12. 2025Calibrating LLMs for Text-to-SQL Parsing by Leveraging Sub-clause Frequenciescited by 5
9 more
  1. 2025SAFE-SQL: Self-Augmented In-Context Learning with Fine-grained Example Selection for Text-to-SQLcited by 4
  2. 2026FlexSQL: Flexible Exploration and Execution Make Better Text-to-SQL Agentscited by 3
  3. 2025AGENTIQL: An Agent-Inspired Multi-Expert Framework for Text-to-SQL Generationcited by 2
  4. 2025Beyond Query-Level Comparison: Fine-Grained Reinforcement Learning for Text-to-SQL with Automated Interpretable Critiquescited by 2
  5. 2026EGREFINE: An Execution-Grounded Optimization Framework for Text-to-SQL Schema Refinementcited by 1
  6. 2023dIR -- Discrete Information Retrieval: Conversational Search over Unstructured (and Structured) Data with Large Language Modelscited by 0
  7. 2025Bridging Global Intent with Local Details: A Hierarchical Representation Approach for Semantic Validation in Text-to-SQLcited by 0
  8. 2026PleaSQLarify: Visual Pragmatic Repair for Natural Language Database Queryingcited by 0
  9. 2026GGC: Selective Query Correction for Reliable Text-to-SPARQL Generationcited by 0