This article examines the evolution of Text-to-SQL, introduces the J‑Schema representation and chain-of-thought prompting, applies iterative DPO training and self-consistency voting, and demonstrates how these techniques raise execution accuracy on the BIRD benchmark from 56.6% to 69.2%.
This paper presents a comprehensive approach to improve Text‑to‑SQL performance by introducing J‑Schema for structured database representation, leveraging chain‑of‑thought prompting, applying iterative DPO training, and employing self‑consistency voting, achieving execution accuracy gains from 56.6% to 69.2% on the BIRD benchmark.
This article presents a comprehensive study on improving Text-to-SQL performance by introducing J‑Schema for structured schema representation, applying iterative Direct Preference Optimization (DPO) training, and leveraging self‑consistency voting mechanisms, achieving up to a 12% accuracy gain on the BIRD benchmark.
This article surveys the evolution of Text-to-SQL, introduces the J‑Schema representation and chain-of-thought prompting, details an iterative DPO training pipeline with hyper‑parameter tuning, and demonstrates how self‑consistency voting boosts execution accuracy on the BIRD benchmark from 56.6% to 69.2%.
