Computer Science > Computation and Language
[Submitted on 3 Jul 2024 (v1), last revised 4 Nov 2024 (this version, v2)]
Title:Improving Retrieval-augmented Text-to-SQL with AST-based Ranking and Schema Pruning
View PDF HTML (experimental)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.
Submission history
From: Zhili Shen [view email][v1] Wed, 3 Jul 2024 15:55:14 UTC (375 KB)
[v2] Mon, 4 Nov 2024 12:14:13 UTC (373 KB)
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