基于轨迹预算与选择性优化的Text2Cypher自适应测试时推理

中文摘要 AI 辅助

大语言模型已为结构化数据库提供了自然语言接口，但生成的查询仍可能包含语法错误、违反数据库模式或在执行时失败。测试时推理策略无需额外训练即可提升生成可靠性，但现有方法常使用固定推理预算与统一优化策略，导致不同复杂度问题产生不必要的计算。本研究针对Text2Cypher开展自适应测试时推理研究，提出两种策略：自适应轨迹预算，即根据问题难度动态调整候选生成预算；选择性执行引导优化，即仅在额外推理有望带来增益时应用修正。在Gemma-2-9B与Qwen-2.5-7B上的实验表明，自适应轨迹预算可将平均生成预算降低30.7%、 wall-clock推理时间缩短21-25%，同时保持相当的生成质量；选择性优化保留了几乎全部全量优化的执行成功增益，仅使执行成功率降低0.2-0.5%，同时避免了对更简单问题的不必要优化。实验还显示，单一修正模型（Gemma-4）可有效优化不同模型家族的输出，表明优化可跨家族迁移。

英文摘要

Large language models have enabled natural language interfaces for structured databases, but generated queries may still contain syntactic errors, violate database schemas, or fail during execution. Test-time inference strategies improve generation reliability without additional training, but existing approaches often use fixed inference budgets and uniform refinement strategies, leading to unnecessary computation across questions with different complexity levels. In this work, we investigate adaptive test-time inference for Text2Cypher and introduce two strategies: adaptive trace budgeting, which dynamically adjusts the candidate generation budget based on question difficulty, and selective execution-guided refinement, which applies correction only when additional inference is expected to be beneficial. Experiments on Gemma-2-9B and Qwen-2.5-7B show that adaptive trace budgeting reduces the average generation budget by 30.7% and wall-clock inference time by 21-25% while maintaining comparable generation quality. Selective refinement preserves nearly all execution success gains of full refinement, reducing execution success by only 0.2-0.5% while avoiding unnecessary refinement for simpler questions. Experiments show that a single correction model (Gemma-4) effectively refines outputs from a different model family, suggesting refinement transfers across families.

发表机构

• Neo4j

机构由 AI 辅助整理，请以论文原文为准。