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

2023/05/22 by Ziru Chen, Chen, Ziru, Shijie Chen +13 · 6 citations
Computer Science · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #Databases (cs.DB) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Natural Language Processing Techniques #Topic Modeling #Web Data Mining and Analysis

paper · pdf · doi:10.48550/arxiv.2305.13073

openalex publication_date 2023/05/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Cited by

• Bridging Global Intent with Local Details: A Hierarchical Representation Approach for Semantic Validation in Text-to-SQL
• Beyond Query-Level Comparison: Fine-Grained Reinforcement Learning for Text-to-SQL with Automated Interpretable Critiques
• Can LLMs Narrate Tabular Data? An Evaluation Framework for Natural Language Representations of Text-to-SQL System Outputs
• AGENTIQL: An Agent-Inspired Multi-Expert Framework for Text-to-SQL Generation
• GGC: Selective Query Correction for Reliable Text-to-SPARQL Generation
• AmbiSQL: Interactive Ambiguity Detection and Resolution for Text-to-SQL

Related

• Correcting Semantic Parses with Natural Language through Dynamic Schema Encoding
• SQLens: An End-to-End Framework for Error Detection and Correction in Text-to-SQL
• Error Detection for Text-to-SQL Semantic Parsing
• Speak to your Parser: Interactive Text-to-SQL with Natural Language Feedback
• SQLCritic: Correcting Text-to-SQL Generation via Clause-wise Critic