Improving Retrieval-augmented Text-to-SQL with AST-based Ranking and Schema Pruning

• URL: http://arxiv.org/abs/2407.03227v2
• Date: Mon, 04 Nov 2024 12:14:13 GMT
• Title: Improving Retrieval-augmented Text-to-SQL with AST-based Ranking and Schema Pruning
• Authors: Zhili Shen, Pavlos Vougiouklis, Chenxin Diao, Kaustubh Vyas, Yuanyi Ji, Jeff Z. Pan,
• Abstract summary: We focus on Text-to- semantic parsing from the perspective of retrieval-augmented generation. Motivated by challenges related to the size of commercial database schemata and the deployability of business intelligence solutions, we propose $textASTReS$ that dynamically retrieves input database information.
• Score: 10.731045939849125
• License:
• Abstract: We focus on Text-to-SQL semantic parsing from the perspective of retrieval-augmented generation. Motivated by challenges related to the size of commercial database schemata and the deployability of business intelligence solutions, we propose $\text{ASTReS}$ that dynamically retrieves input database information and uses abstract syntax trees to select few-shot examples for in-context learning. Furthermore, we investigate the extent to which an in-parallel semantic parser can be leveraged for generating approximated versions of the expected SQL queries, to support our retrieval. We take this approach to the extreme--we adapt a model consisting of less than $500$M parameters, to act as an extremely efficient approximator, enhancing it with the ability to process schemata in a parallelised manner. We apply $\text{ASTReS}$ to monolingual and cross-lingual benchmarks for semantic parsing, showing improvements over state-of-the-art baselines. Comprehensive experiments highlight the contribution of modules involved in this retrieval-augmented generation setting, revealing interesting directions for future work.

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