Evaluating the Text-to-SQL Capabilities of Large Language Models

Abstract: We perform an empirical evaluation of Text-to-SQL capabilities of the Codex LLM. 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.