mlsanityData to 14 Oct 2026

SQaLe: A Large Text-to-SQL Corpus Grounded in Real Schemas

arXiv 2602.22223 ↗First posted 16 Dec 2025cs.IR cs.CL cs.LGcited by 3

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Abstract (arXiv)

Advances in large language models have accelerated progress in text-to-SQL, methods for converting natural language queries into valid SQL queries. A key bottleneck for developing generalizable text-to-SQL models is the lack of large-scale datasets with sufficient schema and query complexity, domain coverage, and task diversity. We introduce SQaLe: a large-scale semi-synthetic text-to-SQL dataset built on 135,875 relational database schemas expanded from a collection of real-world schemas, SchemaPile. We establish a principled generation pipeline which combines schema sampling, question synthesis, and SQL construction, and produce 517,676 high-quality (question, schema, query) triples. The SQaLe dataset captures realistic schema size variability, diverse query patterns, and natural language ambiguity while maintaining execution validity. We provide an analysis of its contents and characteristics, and find that SQaLe introduces the most realistic large-scale text-to-SQL dataset to date in comparison with existing benchmarks and datasets. We discuss how SQaLe enables our vision for data scaling and model generalization in text-to-SQL research. The dataset is accessible at: https://huggingface.co/datasets/trl-lab/SQaLe-text-to-SQL-dataset.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 19 of its 33 references that are in the map. First posted on arXiv 16 Dec 2025.

Builds on

19 of 33 references.

References · 19

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2020Scaling Laws for Neural Language Modelscited by 6,586
  3. 2022Training Compute-Optimal Large Language Modelscited by 3,019
  4. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
  5. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,139
  6. 2023Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLscited by 688
  7. 2023Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluationcited by 385
  8. 2024Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflowscited by 232
  9. 2021KaggleDBQA: Realistic Evaluation of Text-to-SQL Parserscited by 106
  10. 2025OmniSQL: Synthesizing High-quality Text-to-SQL Data at Scalecited by 91
  11. 2019Text-to-SQL Generation for Question Answering on Electronic Medical Recordscited by 77
  12. 2024Datasets for Large Language Models: A Comprehensive Surveycited by 71
7 more
  1. 2024A Survey of Text-to-SQL in the Era of LLMs: Where are we, and where are we going?cited by 56
  2. 2024Synthesizing Text-to-SQL Data from Weak and Strong LLMscited by 54
  3. 2023EHRSQL: A Practical Text-to-SQL Benchmark for Electronic Health Recordscited by 45
  4. 2024BEAVER: An Enterprise Benchmark for Text-to-SQLcited by 42
  5. 2023Benchmarking and Improving Text-to-SQL Generation under Ambiguitycited by 34
  6. 2023UNITE: A Unified Benchmark for Text-to-SQL Evaluationcited by 14
  7. 2025Exploring Database Normalization Effects on SQL Generationcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Human-Level Text-to-SQL via Reinforcement Learning on Verified Data, Without Pipeline Engineeringcited by 1
  2. 2026Automated IEP Generation from Traditional Chinese Parent-Teacher Interviews via Corpus-Grounded Feature Diffusioncited by 0
  3. 2026BI-Agent and BI-Bench: Towards Automating End-to-End Business Intelligencecited by 0