mlsanityData to 7 Oct 2026

Deep Learning Driven Natural Languages Text to SQL Query Conversion: A Survey

arXiv 2208.04415 ↗First posted 8 Aug 2022cs.CL cs.AIcited by 11

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Abstract (arXiv)

With the future striving toward data-centric decision-making, seamless access to databases is of utmost importance. There is extensive research on creating an efficient text-to-sql (TEXT2SQL) model to access data from the database. Using a Natural language is one of the best interfaces that can bridge the gap between the data and results by accessing the database efficiently, especially for non-technical users. It will open the doors and create tremendous interest among users who are well versed in technical skills or not very skilled in query languages. Even if numerous deep learning-based algorithms are proposed or studied, there still is very challenging to have a generic model to solve the data query issues using natural language in a real-work scenario. The reason is the use of different datasets in different studies, which comes with its limitations and assumptions. At the same time, we do lack a thorough understanding of these proposed models and their limitations with the specific dataset it is trained on. In this paper, we try to present a holistic overview of 24 recent neural network models studied in the last couple of years, including their architectures involving convolutional neural networks, recurrent neural networks, pointer networks, reinforcement learning, generative models, etc. We also give an overview of the 11 datasets that are widely used to train the models for TEXT2SQL technologies. We also discuss the future application possibilities of TEXT2SQL technologies for seamless data queries.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 39 of its 72 references that are in the map. First posted on arXiv 8 Aug 2022.

Builds on

39 of 72 references.

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27 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 5; 2025: 1; 2026: 2.

11 papers.

  1. 2024Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQLcited by 118
  2. 2023Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Codecited by 62
  3. 2024SQLBench: A Comprehensive Evaluation for Text-to-SQL Capabilities of Large Language Modelscited by 32
  4. 2024From Natural Language to SQL: Review of LLM-based Text-to-SQL Systemscited by 15
  5. 2024Enhancing LLM Fine-tuning for Text-to-SQLs by SQL Quality Measurementcited by 3
  6. 2024BIS: NL2SQL Service Evaluation Benchmark for Business Intelligence Scenarioscited by 3
  7. 2026SQLMorph: Query Mutation and Fine-Grained Metrics for Text-to-SQL Evaluationcited by 1
  8. 2023Text2Cohort: Facilitating Intuitive Access to Biomedical Data with Natural Language Cohort Discoverycited by 0
  9. 2023Saturn Platform: Foundation Model Operations and Generative AI for Financial Servicescited by 0
  10. 2025Exploring Generative AI Techniques in Government: A Case Studycited by 0
  11. 2026A Hybrid Agentic AI Framework for Intelligent Supply Chain Analyticscited by 0