OraPlan-SQL: A Planning-Centric Framework for Complex Bilingual NL2SQL Reasoning

TL;DR

OraPlan-SQL is a novel bilingual NL2SQL system that uses a feedback-guided, single-planner approach with entity-linking and plan diversification, achieving top accuracy in complex reasoning tasks.

Contribution

Introduces a feedback-guided meta-prompting strategy and entity-linking for improved bilingual NL2SQL reasoning without multi-agent orchestration.

Findings

Achieved over 55% execution accuracy in English and Chinese.

Maintained over 99% SQL validity across benchmarks.

Outperformed prior systems by more than 6% in accuracy.

Abstract

We present OraPlan-SQL, our system for the Archer NL2SQL Evaluation Challenge 2025, a bilingual benchmark requiring complex reasoning such as arithmetic, commonsense, and hypothetical inference. OraPlan-SQL ranked first, exceeding the second-best system by more than 6% in execution accuracy (EX), with 55.0% in English and 56.7% in Chinese, while maintaining over 99% SQL validity (VA). Our system follows an agentic framework with two components: Planner agent that generates stepwise natural language plans, and SQL agent that converts these plans into executable SQL. Since SQL agent reliably adheres to the plan, our refinements focus on the planner. Unlike prior methods that rely on multiple sub-agents for planning and suffer from orchestration overhead, we introduce a feedback-guided meta-prompting strategy to refine a single planner. Failure cases from a held-out set are clustered with human input, and an LLM distills them into corrective guidelines that are integrated into the planner's system prompt, improving generalization without added complexity. For the multilingual scenario, to address transliteration and entity mismatch issues, we incorporate entity-linking guidelines that generate alternative surface forms for entities and explicitly include them in the plan. Finally, we enhance reliability through plan diversification: multiple candidate plans are generated for each query, with the SQL agent producing a query for each plan, and final output selected via majority voting over their executions.