# RB-SQL: A Retrieval-based LLM Framework for Text-to-SQL

arXiv: https://arxiv.org/abs/2407.08273 · HTML: https://mlsanity.com/paper/2407.08273/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2024.

## Built on by (most cited first, 3 of 3)

- [nvAgent: Automated Data Visualization from Natural Language via Collaborative Agent Workflow](https://mlsanity.com/paper/2502.05036/) (2025)
- [Leveraging Prior Experience: An Expandable Auxiliary Knowledge Base for Text-to-SQL](https://mlsanity.com/paper/2411.13244/) (2024)
- [TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering](https://mlsanity.com/paper/2509.01312/) (2025)

## Abstract (arXiv)

Large language models (LLMs) with in-context learning have significantly improved the performance of text-to-SQL task. Previous works generally focus on using exclusive SQL generation prompt to improve the LLMs' reasoning ability. However, they are mostly hard to handle large databases with numerous tables and columns, and usually ignore the significance of pre-processing database and extracting valuable information for more efficient prompt engineering. Based on above analysis, we propose RB-SQL, a novel retrieval-based LLM framework for in-context prompt engineering, which consists of three modules that retrieve concise tables and columns as schema, and targeted examples for in-context learning. Experiment results demonstrate that our model achieves better performance than several competitive baselines on public datasets BIRD and Spider.