Squrve: A Unified and Modular Framework for Complex Real-World Text-to-SQL Tasks
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Abstract (arXiv)
Text-to-SQL technology has evolved rapidly, with diverse academic methods achieving impressive results. However, deploying these techniques in real-world systems remains challenging due to limited integration tools. Despite these advances, we introduce Squrve, a unified, modular, and extensive Text-to-SQL framework designed to bring together research advances and real-world applications. Squrve first establishes a universal execution paradigm that standardizes invocation interfaces, then proposes a multi-actor collaboration mechanism based on seven abstracted effective atomic actor components. Experiments on widely adopted benchmarks demonstrate that the collaborative workflows consistently outperform the original individual methods, thereby opening up a new effective avenue for tackling complex real-world queries. The codes are available at https://github.com/Satissss/Squrve.
Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Builds on 26 of its 39 references that are in the map. First posted on arXiv 28 Oct 2025.
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26 of 39 references.
References · 26
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,304
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,322
- 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,133
- 2023Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLscited by 680
- 2023DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correctioncited by 472
- 2020TaBERT: Pretraining for Joint Understanding of Textual and Tabular Datacited by 397
- 2023Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluationcited by 383
- 2023RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQLcited by 194
- 2024CHESS: Contextual Harnessing for Efficient SQL Synthesiscited by 167
- 2023MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQLcited by 162
- 2024CHASE-SQL: Multi-Path Reasoning and Preference Optimized Candidate Selection in Text-to-SQLcited by 137
- 2020Structure-Grounded Pretraining for Text-to-SQLcited by 126
14 more
- 2024Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQLcited by 119
- 2021Towards Robustness of Text-to-SQL Models against Synonym Substitutioncited by 110
- 2024MCS-SQL: Leveraging Multiple Prompts and Multiple-Choice Selection For Text-to-SQL Generationcited by 105
- 2023Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL Parsingcited by 93
- 2021Exploring Underexplored Limitations of Cross-Domain Text-to-SQL Generalizationcited by 92
- 2022Coder Reviewer Reranking for Code Generationcited by 84
- 2024A Survey on Employing Large Language Models for Text-to-SQL Taskscited by 67
- 2020RYANSQL: Recursively Applying Sketch-based Slot Fillings for Complex Text-to-SQL in Cross-Domain Databasescited by 63
- 2025OpenSearch-SQL: Enhancing Text-to-SQL with Dynamic Few-shot and Consistency Alignmentcited by 53
- 2022Proton: Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL Parsingcited by 30
- 2020AutoQA: From Databases To QA Semantic Parsers With Only Synthetic Training Datacited by 29
- 2022Towards Robustness of Text-to-SQL Models Against Natural and Realistic Adversarial Table Perturbationcited by 28
- 2025LinkAlign: Scalable Schema Linking for Real-World Large-Scale Multi-Database Text-to-SQLcited by 27
- 2021Improving Text-to-SQL with Schema Dependency Learningcited by 25
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