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Retrieval and Augmentation of Domain Knowledge for Text-to-SQL Semantic Parsing

arXiv 2510.02394 ↗First posted 1 Oct 2025cs.CLcited by 0

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Abstract (arXiv)

The performance of Large Language Models (LLMs) for translating Natural Language (NL) queries into SQL varies significantly across databases (DBs). NL queries are often expressed using a domain specific vocabulary, and mapping these to the correct SQL requires an understanding of the embedded domain expressions, their relationship to the DB schema structure. Existing benchmarks rely on unrealistic, ad-hoc query specific textual hints for expressing domain knowledge. In this paper, we propose a systematic framework for associating structured domain statements at the database level. We present retrieval of relevant structured domain statements given a user query using sub-string level match. We evaluate on eleven realistic DB schemas covering diverse domains across five open-source and proprietary LLMs and demonstrate that (1) DB level structured domain statements are more practical and accurate than existing ad-hoc query specific textual domain statements, and (2) Our sub-string match based retrieval of relevant domain statements provides significantly higher accuracy than other retrieval approaches.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 29 of its 34 references that are in the map. First posted on arXiv 1 Oct 2025.

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References · 29

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  2. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,139
  3. 2017SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluationcited by 1,001
  4. 2023Large Language Models Can Be Easily Distracted by Irrelevant Contextcited by 841
  5. 2023Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLscited by 688
  6. 2017Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learningcited by 687
  7. 2021Learning To Retrieve Prompts for In-Context Learningcited by 648
  8. 2023DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correctioncited by 477
  9. 2021ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interactioncited by 476
  10. 2023Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluationcited by 385
  11. 2024Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflowscited by 232
  12. 2023RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQLcited by 195
17 more
  1. 2024CodeS: Towards Building Open-source Language Models for Text-to-SQLcited by 187
  2. 2024CHASE-SQL: Multi-Path Reasoning and Preference Optimized Candidate Selection in Text-to-SQLcited by 138
  3. 2021Towards Robustness of Text-to-SQL Models against Synonym Substitutioncited by 110
  4. 2021KaggleDBQA: Realistic Evaluation of Text-to-SQL Parserscited by 106
  5. 2023Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL Parsingcited by 93
  6. 2021Exploring Underexplored Limitations of Cross-Domain Text-to-SQL Generalizationcited by 92
  7. 2024The Dawn of Natural Language to SQL: Are We Fully Ready?cited by 79
  8. 2023Coverage-based Example Selection for In-Context Learningcited by 59
  9. 2023Skill-Based Few-Shot Selection for In-Context Learningcited by 46
  10. 2024A Preview of XiYan-SQL: A Multi-Generator Ensemble Framework for Text-to-SQLcited by 46
  11. 2024DTS-SQL: Decomposed Text-to-SQL with Small Large Language Modelscited by 45
  12. 2023Unsupervised Dense Retrieval with Relevance-Aware Contrastive Pre-Trainingcited by 35
  13. 2024Knowledge-to-SQL: Enhancing SQL Generation with Data Expert LLMcited by 35
  14. 2023Improving Generalization in Language Model-Based Text-to-SQL Semantic Parsing: Two Simple Semantic Boundary-Based Techniquescited by 24
  15. 2023Towards Knowledge-Intensive Text-to-SQL Semantic Parsing with Formulaic Knowledgecited by 18
  16. 2023UNITE: A Unified Benchmark for Text-to-SQL Evaluationcited by 14
  17. 2018An End-to-end Neural Natural Language Interface for Databasescited by 5

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