# Text-to-SQL based on Large Language Models and Database Keyword Search

arXiv: https://arxiv.org/abs/2501.13594 · HTML: https://mlsanity.com/paper/2501.13594/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2025.

## Built on by (most cited first, 2 of 2)

- [SteinerSQL: Graph-Guided Mathematical Reasoning for Text-to-SQL Generation](https://mlsanity.com/paper/2509.19623/) (2025)
- [SAFAARI: Schema-Aware Framework for Accelerated Advertiser Response Intelligence](https://mlsanity.com/paper/2607.25042/) (2026)

## Abstract (arXiv)

Text-to-SQL prompt strategies based on Large Language Models (LLMs) achieve remarkable performance on well-known benchmarks. However, when applied to real-world databases, their performance is significantly less than for these benchmarks, especially for Natural Language (NL) questions requiring complex filters and joins to be processed. This paper then proposes a strategy to compile NL questions into SQL queries that incorporates a dynamic few-shot examples strategy and leverages the services provided by a database keyword search (KwS) platform. The paper details how the precision and recall of the schema-linking process are improved with the help of the examples provided and the keyword-matching service that the KwS platform offers. Then, it shows how the KwS platform can be used to synthesize a view that captures the joins required to process an input NL question and thereby simplify the SQL query compilation step. The paper includes experiments with a real-world relational database to assess the performance of the proposed strategy. The experiments suggest that the strategy achieves an accuracy on the real-world relational database that surpasses state-of-the-art approaches. The paper concludes by discussing the results obtained.