mlsanityData to 7 Oct 2026

Blar-SQL: Faster, Stronger, Smaller NL2SQL

arXiv 2401.02997 ↗First posted 4 Jan 2024cs.CL cs.AIcited by 4

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Abstract (arXiv)

Large Language Models (LLMs) have gained considerable notoriety in the field of natural language to SQL tasks (NL2SQL). In this study, we show how task decomposition can greatly benefit LLMs in database understanding and query generation in order to answer human questions with an SQL query. We fined-tuned open source models, specifically Llama-2 and Code Llama, by combining 2 different models each designated to focus on one of two tasks in order to leverage each model's core competency to further increase the accuracy of the final SQL query. We propose a new framework to divide the schema into chunks in order to fit more information into a limited context. Our results are comparable with those obtained by GPT-4 at the same time being 135 times smaller, 90 times faster and more than 100 times cheaper than GPT-4.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 8 of its 9 references that are in the map. First posted on arXiv 4 Jan 2024.

Builds on

8 of 9 references.

References · 8

  1. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  2. 2023Code Llama: Open Foundation Models for Codecited by 2,105
  3. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,120
  4. 2017Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learningcited by 679
  5. 2023Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLscited by 667
  6. 2022Decomposed Prompting: A Modular Approach for Solving Complex Taskscited by 554
  7. 2023DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correctioncited by 465
  8. 2023Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluationcited by 378

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Cited by year

Citing papers per year: 2024: 2; 2025: 2.

4 papers.

  1. 2024DTS-SQL: Decomposed Text-to-SQL with Small Large Language Modelscited by 45
  2. 2025Is Long Context All You Need? Leveraging LLM's Extended Context for NL2SQLcited by 30
  3. 2024SQL-Encoder: Improving NL2SQL In-Context Learning Through a Context-Aware Encodercited by 4
  4. 2025Toward Real-World Table Agents: Capabilities, Workflows, and Design Principles for LLM-based Table Intelligencecited by 2