Summary of Sql-gen: Bridging the Dialect Gap For Text-to-sql Via Synthetic Data and Model Merging, by Mohammadreza Pourreza et al.
SQL-GEN: Bridging the Dialect Gap for Text-to-SQL Via Synthetic Data And Model Merging
by Mohammadreza Pourreza, Ruoxi Sun, Hailong Li, Lesly Miculicich, Tomas Pfister, Sercan O. Arik
First submitted to arxiv on: 22 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Databases (cs.DB); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces SQL-GEN, a framework for generating synthetic training data for any SQL dialect. The framework is guided by readily available dialect-specific tutorials and improves cross-dialect Text-to-SQL performance by up to 20% over existing methods. Additionally, combining synthetic data with human-annotated data yields further improvements of up to 5.6%. The authors also propose a novel Mixture-of-Experts (MoE) initialization that leverages shared knowledge across dialects, resulting in a versatile model optimized for multiple SQL dialects. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a tool called SQL-GEN to help machines learn how to turn text into SQL code, but it only works with certain types of SQL. The authors make this work better by using tutorials that explain the different types of SQL and then test their tool on real data. They also come up with a new way to combine information from these different tutorials to make the machine learning model even better. |
Keywords
* Artificial intelligence * Machine learning * Mixture of experts * Synthetic data
