EviLink通过多路径推理与不确定性引导，精准高效地完成大规模文本转SQL的模式链接。
EviLink: Multi-Path Schema Linking with Uncertainty-Guided Evidence Acquisition for Large-Scale Text-to-SQL
- 构建多假设模式定位，动态识别不同查询路径所需的表字段。
- 在Spider2-Snow上实现90.15%字段级严格召回率，平均仅用12.33万词元。
- 适合需要高精度、低开销的复杂自然语言转SQL系统开发者使用。
模式链接是大规模文本转SQL中的关键挑战，需从庞大模糊的数据库中识别出紧凑且充分的模式上下文。现有方法常将模式链接视为单一SQL路径上的确定性选择，但复杂查询可能对应多个有效实现，所需模式各异。本文将模式链接重新建模为对多条合理SQL路径的不确定性感知模式需求推理，区分必需的模式项与路径依赖的不确定项，并仅在必要时获取证据。我们提出EviLink模型，融合多假设模式定位与不确定性引导的证据获取机制。在BIRD-Dev和Spider2-Snow上的实验表明，该方法显著提升了模式完整度、相关性与词元成本之间的平衡。在Spider2-Snow上，EviLink达到90.15%的字段级严格召回率，平均使用123.30K词元，在固定生成器下进一步提升下游SQL生成性能。
原文摘要 · Abstract (English)
Schema linking is a difficult and important step in large-scale Text-to-SQL, where systems must identify a compact yet sufficient schema context from large and ambiguous databases. Existing methods often treat schema linking as deterministic selection around a single SQL path, but complex questions may admit multiple valid realizations with different schema needs. We reframe schema linking as uncertainty-aware schema-need inference over multiple plausible SQL paths, where the system distinguishes required schema items from path-dependent uncertain ones and acquires evidence only where needed. We instantiate this reframing with EviLink, which combines multi-hypothesis schema grounding with uncertainty-guided evidence acquisition. Experiments on BIRD-Dev and Spider2-Snow show that this perspective improves the balance among schema completeness, schema relevance, and token cost. On Spider2-Snow, EviLink achieves 90.15% field-level strict recall rate, uses 123.30K average tokens, and improves downstream SQL generation under a fixed generator.
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