ESQ‑Bench is an Oracle‑first NL2SQL benchmark that introduces systematic complexity tiers and silent‑divergence evaluation across three enterprise schema levels. It provides six populated schemas (465 tables, 164,682 rows) on Oracle, PostgreSQL, MySQL, and SQL Server, along with 550 gold‑validated question‑query pairs and a four‑metric evaluation harness. The benchmark reveals that state‑of‑the‑art models such as GPT‑4o and Claude Sonnet 4.6 experience significant drops in execution accuracy and high silent‑divergence rates as schema complexity increases, highlighting a gap between closed‑API models and open‑weight baselines on enterprise Oracle schemas.
By Sanjay Mishra, Divya Chukkapalli, Ganesh R. Naik
The paper introduces DRL, a deterministic relational middleware layer designed to enable transaction-safe natural‑language to SQL (NL2SQL) interfaces over large enterprise OLTP catalogs. DRL interposes between front‑ends and SQL back‑ends, employing dynamic context pruning, relational AST typing, and transactional safeguards (EXPLAIN gating and NULL guards) to keep context within LLM attention limits and detect silent divergence. Experiments on PostgreSQL and MySQL show significant context reductions (up to 92%) and high execution match rates (≈53%) for GPT‑4o, Claude Sonnet 4.5, and Gemini 2.5 Flash, while also revealing that evaluation code quality can materially affect reported performance gaps.
By Sanjay Mishra, Divya Chukkapalli, Ganesh R. Naik
arXiv:2609.14652v1 Announce Type: cross
Abstract: Large Language Model (LLM) applications often transfer domain concepts into the model's context informally, through prompt prose, schema dumps, and e...
By Blake G. Fitch
arXiv:2606. 12387v1 Announce Type: cross Abstract: Large Language Models (LLMs) have democratized database access through Text-to-SQL, but moving from prototypes to production remains difficult.
By Zhiyi Chen, Jie Song, Peng Li
arXiv:2608. 13612v1 Announce Type: new Abstract: Natural-language interfaces to enterprise data must translate underspecified requests into governed, executable behavior while controlling invalid queries, policy failures, cost, and nondeterminism.
By Bruno Santos Teixeira
arXiv:2608. 11889v1 Announce Type: cross Abstract: Prompting-based (\textit{i}.
By Anik Pramanik, Murat Kantarcioglu, Vincent Oria, Shantanu Sharma
arXiv:2608. 15145v1 Announce Type: new Abstract: Large Language Models (LLMs) have been increasingly adopted in Text-to-SQL systems, yet SQL errors remain a major obstacle in real-world Text-to-SQL inference pipelines.
By Xinmei Huang, Jie Song, Peng Li, Fuxin Jiang, Jing Zhang, Tieying Zhang, Jianjun Chen, Chenming Liu, Tao Yang, Maoyin Liu, Wenda Li, Hong Chen, Cuiping Li
arXiv:2608. 06331v1 Announce Type: cross Abstract: From natural-language query interfaces to automated report generation, data analysis tools need a description of the data: the real-world entities it contains, which columns function as measures or identifiers, and how tables connect into units of analysis.
By Donna Hooshmand, Shubham Shahi, Cameron Barrie, Abhratanu Dutta, Marko Sterbentz, Harper Pack, Kristian J. Hammond
Natural-language interfaces to enterprise data must translate underspecified requests into governed, executable behavior while controlling invalid queries, policy failures, cost, and nondeterminism. SemPlan Benchmark evaluates this architectural design space with a deterministic synthetic bilingual benchmark containing 1,800 cases in English and Brazilian Portuguese; 1,200 cases form the frozen scientific evaluation subset.
arXiv:2602. 16720v2 Announce Type: replace-cross Abstract: Text-to-SQL systems powered by Large Language Models have excelled on academic benchmarks but struggle in complex enterprise environments.
By Bowen Cao, Weibin Liao, Yushi Sun, Dong Fang, Haitao Li, Wai Lam
The paper examines the role of schema linking in Text-to-SQL systems and finds that recent large language models can effectively use relevant schema elements even when many irrelevant ones are present. Consequently, the authors eliminate schema linking when the entire schema fits within the model’s context window, instead employing augmentation, selection, and correction techniques to enhance accuracy. Their approach achieves first place on the BIRD benchmark with a 71.83% accuracy.
By Karime Maamari, Fadhil Abubaker, Daniel Jaroslawicz, Amine Mhedhbi
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.
By Adarsh Agrawal, Shashank Indukuri