mlsanityData to 13 Oct 2026

SQL-to-Schema Enhances Schema Linking in Text-to-SQL

arXiv 2405.09593 ↗First posted 15 May 2024cs.DB cs.AIcited by 12

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Abstract (arXiv)

In sophisticated existing Text-to-SQL methods exhibit errors in various proportions, including schema-linking errors (incorrect columns, tables, or extra columns), join errors, nested errors, and group-by errors. Consequently, there is a critical need to filter out unnecessary tables and columns, directing the language models attention to relevant tables and columns with schema-linking, to reduce errors during SQL generation. Previous approaches have involved sorting tables and columns based on their relevance to the question, selecting the top-ranked ones for sorting, or directly identifying the necessary tables and columns for SQL generation. However, these methods face challenges such as lengthy model training times, high consumption of expensive GPT-4 tokens in few-shot prompts, or suboptimal performance in schema linking. Therefore, we propose an inventive schema linking method in two steps: Firstly, generate an initial SQL query by utilizing the complete database schema. Subsequently, extract tables and columns from the initial SQL query to create a concise schema. Using CodeLlama-34B, when comparing the schemas obtained by mainstream methods with ours for SQL generation, our schema performs optimally. Leveraging GPT4, our SQL generation method achieved results that are comparable to mainstream Text-to-SQL methods on the Spider dataset.

Key facts. Cited by 12 papers in the map (data through 13 Oct 2026). Builds on 15 of its 17 references that are in the map. First posted on arXiv 15 May 2024.

Builds on

15 of 17 references.

References · 15

  1. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,133
  2. 2023DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correctioncited by 472
  3. 2019RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parserscited by 422
  4. 2023Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluationcited by 383
  5. 2019Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representationcited by 210
  6. 2023RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQLcited by 194
  7. 2023C3: Zero-shot Text-to-SQL with ChatGPTcited by 163
  8. 2020Semantic Evaluation for Text-to-SQL with Distilled Test Suitescited by 123
  9. 2023A comprehensive evaluation of ChatGPT's zero-shot Text-to-SQL capabilitycited by 118
  10. 2023Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL Parsingcited by 93
  11. 2018TypeSQL: Knowledge-based Type-Aware Neural Text-to-SQL Generationcited by 81
  12. 2021Natural SQL: Making SQL Easier to Infer from Natural Language Specificationscited by 67
3 more
  1. 2022Prompt Consistency for Zero-Shot Task Generalizationcited by 67
  2. 2023How to Prompt LLMs for Text-to-SQL: A Study in Zero-shot, Single-domain, and Cross-domain Settingscited by 57
  3. 2023Improving Generalization in Language Model-Based Text-to-SQL Semantic Parsing: Two Simple Semantic Boundary-Based Techniquescited by 24

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2024: 2; 2025: 9; 2026: 1.

12 papers.

  1. 2024TimeCMA: Towards LLM-Empowered Multivariate Time Series Forecasting via Cross-Modality Alignmentcited by 103
  2. 2024A Survey on Employing Large Language Models for Text-to-SQL Taskscited by 67
  3. 2025Efficient Multivariate Time Series Forecasting via Calibrated Language Models with Privileged Knowledge Distillationcited by 20
  4. 2025Towards Cross-Modality Modeling for Time Series Analytics: A Survey in the LLM Eracited by 11
  5. 2025SAFE-SQL: Self-Augmented In-Context Learning with Fine-grained Example Selection for Text-to-SQLcited by 4
  6. 2025JOLT-SQL: Joint Loss Tuning of Text-to-SQL with Confusion-aware Noisy Schema Samplingcited by 4
  7. 2025Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunitiescited by 4
  8. 2025Rethinking Schema Linking: A Context-Aware Bidirectional Retrieval Approach for Text-to-SQLcited by 4
  9. 2025End-to-End Text-to-SQL with Dataset Selection: Leveraging LLMs for Adaptive Query Generationcited by 2
  10. 2025Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Taskcited by 1
  11. 2026EviLink: Multi-Path Schema Linking with Uncertainty-Guided Evidence Acquisition for Large-Scale Text-to-SQLcited by 1
  12. 2025GPG: Generalized Policy Gradient Theorem for Transformer-based Policiescited by 0