It discusses Xiaomi DataAgent's third‑place ranking on the global BIRD Text‑to‑SQL benchmark, analyzes challenges such as model hallucination, lack of business knowledge, and complex multi‑table joins, and explains how a semantic harness addresses these problems to enable reliable enterprise data querying.
This article benchmarks twelve Text2SQL models on the BIRD and Spider datasets, analyzes their accuracy, cost, and deployment options, and provides scenario‑specific recommendations to help enterprises and developers choose the most suitable solution.
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.
