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Evaluating the Text-to-SQL Capabilities of Large Language Models
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Evaluating the Text-to-SQL Capabilities of Large Language Models
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We perform an empirical evaluation of Text-to-SQL capabilities of the Codex language model. We find that, without any finetuning, Codex is a strong baseline on the Spider benchmark; we also analyze the failure modes of Codex in this setting. Furthermore, we demonstrate on the GeoQuery and Scholar benchmarks that a small number of in-domain examples provided in the prompt enables Codex to perform better than state-of-the-art models finetuned on such few-shot examples.
Forward citations
Cited by 15 Pith papers
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AV-SQL: Decomposing Complex Text-to-SQL Queries with Agentic Views
AV-SQL uses a pipeline of LLM agents to generate intermediate CTE views that decompose complex Text-to-SQL queries, reaching 70.38% execution accuracy on Spider 2.0.
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SQLStructEval: Structural Evaluation of LLM Text-to-SQL Generation
LLMs produce structurally diverse SQL queries for identical inputs on the Spider benchmark even when execution is correct, and a compile-style pipeline improves both accuracy and structural consistency.
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