OraPlan-SQL：面向复杂双语NL2SQL推理的以规划为核心的框架
OraPlan-SQL: A Planning-Centric Framework for Complex Bilingual NL2SQL Reasoning
- Oracle AI
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对复杂双语NL2SQL推理任务，提出以规划为核心的OraPlan-SQL框架，通过反馈引导元提示优化单规划器、实体链接解决多语言实体问题及规划多样化投票，在2025年Archer挑战赛中夺冠，准确率领先第二名超6%。
AI中文摘要：
我们提出了OraPlan-SQL，这是我们用于2025年Archer NL2SQL评估挑战赛的系统，该赛事是一个需要算术、常识和假设推理等复杂推理能力的双语基准。OraPlan-SQL排名第一，在执行准确率（EX）上超过第二名系统6%以上，其中英文任务为55.0%，中文任务为56.7%，同时保持了99%以上的SQL有效性（VA）。我们的系统采用智能体框架，包含两个组件：生成逐步自然语言规划的规划器智能体，以及将这些规划转换为可执行SQL的SQL智能体。由于SQL智能体能够可靠地遵循规划，我们的优化工作集中在规划器上。与以往依赖多个子智能体进行规划且存在编排开销的方法不同，我们提出了一种反馈引导的元提示策略来优化单个规划器。我们将留出集上的失败案例结合人工输入进行聚类，再由大语言模型（LLM）将其提炼为修正指南并整合到规划器的系统提示中，在不增加复杂度的情况下提升了泛化能力。针对多语言场景，为解决音译和实体不匹配问题，我们加入了实体链接指南，用于生成实体的替代表层形式并将其明确纳入规划中。最后，我们通过规划多样化提升可靠性：为每个查询生成多个候选规划，由SQL智能体为每个规划生成对应的查询，再通过对执行结果进行多数投票选出最终输出。
英文摘要：
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.
