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ACL Anthology · ACL 2026:acl:2026.acl-demo.16 · 记录 #1

Squrve: A Unified and Modular Framework for Complex Real-World Text-to-SQL Tasks

来源日期(仅到月份) 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 22:08
ACL Anthology · ACL 2026 ↗PDF ↗

摘要

Text-to-SQL technology has evolved rapidly, with diverse academic methods achieving impressive results. However, deploying these techniques in real-world systems remains challenging due to limited integration tools. Despite these advances, we introduce Squrve, a unified, modular, and extensive Text-to-SQL framework designed to bring together research advances and real-world applications. Squrve first establishes a universal execution paradigm that standardizes invocation interfaces, then proposes a multi-actor collaboration mechanism based on seven abstracted effective atomic actor components. Experiments on widely adopted benchmarks demonstrate that the collaborative workflows consistently outperform the original individual methods, thereby opening up a new effective avenue for tackling complex real-world queries. The codes are available at https://github.com/LLM-Cube/Squrve.

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记录 #1 · 2026-07 · 最新观测版本

Squrve: A Unified and Modular Framework for Complex Real-World Text-to-SQL Tasks

Text-to-SQL technology has evolved rapidly, with diverse academic methods achieving impressive results. However, deploying these techniques in real-world systems remains challenging due to limited integration tools. Despite these advances, we introduce Squrve, a unified, modular, and extensive Text-to-SQL framework designed to bring together research advances and real-world applications. Squrve first establishes a universal execution paradigm that standardizes invocation interfaces, then proposes a multi-actor collaboration mechanism based on seven abstracted effective atomic actor components. Experiments on widely adopted benchmarks demonstrate that the collaborative workflows consistently outperform the original individual methods, thereby opening up a new effective avenue for tackling complex real-world queries. The codes are available at https://github.com/LLM-Cube/Squrve.

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