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arXiv:2608.29345v1 Announce Type: new Abstract: While recent Large Language Model (LLM)-based text-to-SQL systems achieve impressive performance on standard benchmarks, they struggle when user querie...
Retrieval in the SQL setting has largely been studied as the task of finding, within a large collection of SQL statements, the statement that answers a natural-language question. At scale, however, a more fundamental retrieval problem precedes generation: schema retrieval, identifying the tables and columns a question requires in a database that may contain thousands of them, far more than fit in a model's context.
The paper introduces FLINT, a domain‑specialized Text‑to‑SQL system designed for complex financial databases where data is stored as opaque integer keys. FLINT employs a lookup agent to resolve natural‑language concepts, uses embedding‑based retrieval of query templates from a compact expert‑authored bank, and prunes large schemas by traversing foreign‑key chains. Evaluated on 359 questions across two production financial schemas, FLINT outperforms state‑of‑the‑art baselines and is already deployed in a financial data retrieval service.
Reflect‑SQL is a new framework for converting natural language into SQL queries. It tackles challenges such as large, obscure database schemas, poor table and column retrieval, and syntactically or logically flawed SQL by using a multi‑stage self‑reflection approach. The system iteratively refines queries and SQL through feedback loops driven by an LLM‑as‑a‑judge, achieving 72.03% execution accuracy on the BIRD benchmark, outperforming existing baselines.
arXiv:2606. 28387v1 Announce Type: cross Abstract: Enterprise text-to-SQL systems often fail before SQL is generated: the model receives the wrong schema context.
arXiv:2606. 28601v1 Announce Type: cross Abstract: Recent progress in Text-to-SQL has been driven by stronger language models and prompting strategies, yet performance on real enterprise benchmarks such as Spider 2.