Summary of Improving Retrieval-augmented Text-to-sql with Ast-based Ranking and Schema Pruning, by Zhili Shen and Pavlos Vougiouklis and Chenxin Diao and Kaustubh Vyas and Yuanyi Ji and Jeff Z. Pan
Improving Retrieval-augmented Text-to-SQL with AST-based Ranking and Schema Pruning
by Zhili Shen, Pavlos Vougiouklis, Chenxin Diao, Kaustubh Vyas, Yuanyi Ji, Jeff Z. Pan
First submitted to arxiv on: 3 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Databases (cs.DB)
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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 A novel approach to Text-to-SQL semantic parsing is proposed by retrieving input database information and utilizing abstract syntax trees (ASTs) to select a few-shot examples for in-context learning. The paper, titled , aims to address the limitations of commercial database schemata and deployable business intelligence solutions. By leveraging retrieval-augmented generation, this method dynamically retrieves relevant information from databases and selects optimal examples for training, effectively improving the performance of SQL query generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a new way to understand natural language and create SQL queries. They wanted to solve two big problems: how to handle large amounts of data in commercial databases and how to make business intelligence solutions easy to use. Their solution is called . It works by taking input from the database, organizing it into a special tree-like structure, and then using that structure to select a few examples for training. This approach helps improve the quality of generated SQL queries. |
Keywords
» Artificial intelligence » Few shot » Retrieval augmented generation » Semantic parsing » Syntax
