mlsanityData to 7 Oct 2026

Valid Text-to-SQL Generation with Unification-based DeepStochLog

arXiv 2503.13342 ↗First posted 17 Mar 2025cs.CL cs.AIcited by 3

Not on the map. Why?

Abstract (arXiv)

Large language models have been used to translate natural language questions to SQL queries. Without hard constraints on syntax and database schema, they occasionally produce invalid queries that are not executable. These failures limit the usage of these systems in real-life scenarios. We propose a neurosymbolic framework that imposes SQL syntax and schema constraints with unification-based definite clause grammars and thus guarantees the generation of valid queries. Our framework also builds a bi-directional interface to language models to leverage their natural language understanding abilities. The evaluation results on a subset of SQL grammars show that all our output queries are valid. This work is the first step towards extending language models with unification-based grammars. We demonstrate this extension enhances the validity, execution accuracy, and ground truth alignment of the underlying language model by a large margin. Our code is available at https://github.com/ML-KULeuven/deepstochlog-lm.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 25 of its 32 references that are in the map. First posted on arXiv 17 Mar 2025.

Builds on

25 of 32 references.

References · 25

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2023GPT-4 Technical Reportcited by 18,132
  4. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  5. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  6. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  7. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,120
  8. 2023DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correctioncited by 465
  9. 2019RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parserscited by 420
  10. 2021PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Modelscited by 402
  11. 2023Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluationcited by 378
  12. 2019Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representationcited by 210
13 more
  1. 2023RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQLcited by 191
  2. 2023C3: Zero-shot Text-to-SQL with ChatGPTcited by 159
  3. 2022Synchromesh: Reliable code generation from pre-trained language modelscited by 156
  4. 2020Compositional Generalization and Natural Language Variation: Can a Semantic Parsing Approach Handle Both?cited by 147
  5. 2017SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learningcited by 135
  6. 2023Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL Parsingcited by 93
  7. 2018TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generationcited by 88
  8. 2018TypeSQL: Knowledge-based Type-Aware Neural Text-to-SQL Generationcited by 81
  9. 2018SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-DomainText-to-SQL Taskcited by 74
  10. 2020RYANSQL: Recursively Applying Sketch-based Slot Fillings for Complex Text-to-SQL in Cross-Domain Databasescited by 63
  11. 2021DeepStochLog: Neural Stochastic Logic Programmingcited by 58
  12. 2018Robust Text-to-SQL Generation with Execution-Guided Decodingcited by 51
  13. 2019Grammar-based Neural Text-to-SQL Generationcited by 23

Built on by

Cited by year

Citing papers per year: 2025: 3.

3 papers.

  1. 2025Defining neurosymbolic AIcited by 7
  2. 2025A Learnability Analysis on Neuro-Symbolic Learningcited by 3
  3. 2025DeepProofLog: Efficient Proving in Deep Stochastic Logic Programscited by 0