mlsanityData to 14 Oct 2026

Error Detection for Text-to-SQL Semantic Parsing

arXiv 2305.13683 ↗First posted 23 May 2023cs.CLcited by 12

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Abstract (arXiv)

Despite remarkable progress in text-to-SQL semantic parsing in recent years, the performance of existing parsers is still far from perfect. Specifically, modern text-to-SQL parsers based on deep learning are often over-confident, thus casting doubt on their trustworthiness when deployed for real use. In this paper, we propose a parser-independent error detection model for text-to-SQL semantic parsing. Using a language model of code as its bedrock, we enhance our error detection model with graph neural networks that learn structural features of both natural language questions and SQL queries. We train our model on realistic parsing errors collected from a cross-domain setting, which leads to stronger generalization ability. Experiments with three strong text-to-SQL parsers featuring different decoding mechanisms show that our approach outperforms parser-dependent uncertainty metrics. Our model could also effectively improve the performance and usability of text-to-SQL semantic parsers regardless of their architectures. (Our implementation is available at https://github.com/OSU-NLP-Group/Text2SQL-Error-Detection)

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 24 of its 38 references that are in the map. First posted on arXiv 23 May 2023.

Builds on

24 of 38 references.

References · 24

  1. 2020CodeBERT: A Pre-Trained Model for Programming and Natural Languagescited by 1,310
  2. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,139
  3. 2021How Attentive are Graph Attention Networks?cited by 835
  4. 2020Stanza: A Python Natural Language Processing Toolkit for Many Human Languagescited by 723
  5. 2019RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parserscited by 424
  6. 2021PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Modelscited by 411
  7. 2019Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representationcited by 210
  8. 2023RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQLcited by 195
  9. 2020GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsingcited by 171
  10. 2020Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsingcited by 133
  11. 2021LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relationscited by 119
  12. 2021KaggleDBQA: Realistic Evaluation of Text-to-SQL Parserscited by 106
12 more
  1. 2020SmBoP: Semi-autoregressive Bottom-up Semantic Parsingcited by 97
  2. 2022RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQLcited by 85
  3. 2021Natural SQL: Making SQL Easier to Infer from Natural Language Specificationscited by 68
  4. 2019Global Reasoning over Database Structures for Text-to-SQL Parsingcited by 43
  5. 2019Model-based Interactive Semantic Parsing: A Unified Framework and A Text-to-SQL Case Studycited by 40
  6. 2022Towards Robustness of Text-to-SQL Models Against Natural and Realistic Adversarial Table Perturbationcited by 28
  7. 2020Photon: A Robust Cross-Domain Text-to-SQL Systemcited by 26
  8. 2020An Imitation Game for Learning Semantic Parsers from User Interactioncited by 23
  9. 2023Text-to-SQL Error Correction with Language Models of Codecited by 21
  10. 2020Bertrand-DR: Improving Text-to-SQL using a Discriminative Re-rankercited by 12
  11. 2020"What Do You Mean by That?" A Parser-Independent Interactive Approach for Enhancing Text-to-SQLcited by 11
  12. 2022T5QL: Taming language models for SQL generationcited by 8

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Cited by year (2026 to 14 Oct)

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

12 papers.

  1. 2024When is Tree Search Useful for LLM Planning? It Depends on the Discriminatorcited by 47
  2. 2025SQLens: An End-to-End Framework for Error Detection and Correction in Text-to-SQLcited by 12
  3. 2025Confidence Estimation for Error Detection in Text-to-SQL Systemscited by 11
  4. 2025Can LLMs Narrate Tabular Data? An Evaluation Framework for Natural Language Representations of Text-to-SQL System Outputscited by 7
  5. 2025Calibrating LLMs for Text-to-SQL Parsing by Leveraging Sub-clause Frequenciescited by 5
  6. 2026Semantic Layers for Reliable LLM-Powered Data Analytics: A Paired Benchmark of Accuracy and Hallucination Across Three Frontier Modelscited by 3
  7. 2026ErrorLLM: Modeling SQL Errors for Text-to-SQL Refinementcited by 2
  8. 2023Predicting generalization performance with correctness discriminatorscited by 1
  9. 2025Bridging Global Intent with Local Details: A Hierarchical Representation Approach for Semantic Validation in Text-to-SQLcited by 0
  10. 2026CLARA: Clarification of Language Ambiguity through Result Analysis for Natural-Language Cancer Genomics Queriescited by 0
  11. 2026Certified Against Which Oracle? Execution Labels Set the Reported Risk of Conformal Abstention for Text-to-SQLcited by 0
  12. 2026Why, Where, How: Taxonomy-guided Error Grounding for Code Repair in NL2SQLcited by 0