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Found 4 papers, 1 papers with code
SQL-Encoder: Improving NL2SQL In-Context Learning Through a Context-Aware Encoder
Furthermore, compared to these competitive models, our proposed encoder enhances the downstream performance of NL2SQL models in 1-shot in-context learning scenarios by 1-2\% for GPT-3. 5-turbo, 4-8\% for CodeLlama-7B, and 2-3\% for CodeLlama-13B.
DTS-SQL: Decomposed Text-to-SQL with Small Large Language Models
Leading models for the text-to-SQL task heavily rely on proprietary Large Language Models (LLMs), posing concerns over data privacy.
Evaluating Cross-Domain Text-to-SQL Models and Benchmarks
In this paper, we conduct an extensive study of several prominent cross-domain text-to-SQL benchmarks and re-evaluate some of the top-performing models within these benchmarks, by both manually evaluating the SQL queries and rewriting them in equivalent expressions.
DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction
In particular, we show that breaking down the generation problem into sub-problems and feeding the solutions of those sub-problems into LLMs can be an effective approach for significantly improving their performance.
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