Improving Retrieval-augmented Text-to-SQL with AST-based Ranking and Schema Pruning
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arXiv
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| Format: | Preprint |
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2024
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| author | Shen, Zhili Vougiouklis, Pavlos Diao, Chenxin Vyas, Kaustubh Ji, Yuanyi Pan, Jeff Z. |
| author_facet | Shen, Zhili Vougiouklis, Pavlos Diao, Chenxin Vyas, Kaustubh Ji, Yuanyi Pan, Jeff Z. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_03227 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Improving Retrieval-augmented Text-to-SQL with AST-based Ranking and Schema Pruning Shen, Zhili Vougiouklis, Pavlos Diao, Chenxin Vyas, Kaustubh Ji, Yuanyi Pan, Jeff Z. Computation and Language Artificial Intelligence Databases 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. |
| title | Improving Retrieval-augmented Text-to-SQL with AST-based Ranking and Schema Pruning |
| topic | Computation and Language Artificial Intelligence Databases |
| url | https://arxiv.org/abs/2407.03227 |