# CHESS: Contextual Harnessing for Efficient SQL Synthesis

**Authors:** Shayan Talaei, Mohammadreza Pourreza, Yu-Chen Chang, Azalia Mirhoseini, Amin Saberi  
**arXiv:** [2405.16755](https://arxiv.org/abs/2405.16755)  
**Date:** 2024-05-27  
**Summary page:** https://www.summarizepaper.com/en/paper/2405.16755v2/

## Key points

- Utilizing large language models (LLMs) for text-to-SQL poses challenges with complex schemas
- Incorporating data catalogs and database values effectively remains a hurdle for SQL generation
- A new pipeline has been proposed to retrieve relevant data and context efficiently, select an optimal schema, and synthesize correct SQL queries
- The paper "CHESS: Contextual Harnessing for Efficient SQL Synthesis" emphasizes the importance of preprocessing for efficient information retrieval

## Summary

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Utilizing large language models (LLMs) for transforming natural language questions into SQL queries (text-to-SQL) poses challenges, especially when dealing with real-world databases with complex schemas. Incorporating data catalogs and database values effectively for SQL generation remains a hurdle, leading to suboptimal solutions. To address this issue, a new pipeline has been proposed that retrieves relevant data and context efficiently, selects an optimal schema, and synthesizes correct SQL queries. The paper titled "CHESS: Contextual Harnessing for Efficient SQL Synthesis" by Shayan Talaei et al., highlights the importance of preprocessing in facilitating efficient information retrieval. Utilizing LLMs for text-to-SQL translation poses challenges when dealing with complex schemas. A new pipeline is proposed to retrieve relevant data and context efficiently and generate correct SQL queries. Incorporating data catalogs effectively remains a hurdle in text-to-SQL translation. The proposed pipeline utilizes hierarchical retrieval methods to enhance precision in information retrieval. This research showcases advancements in NLP applied to SQL query generation through the use of large language models.