EviLink: Multi-Path Schema Linking with Uncertainty-Guided Evidence Acquisition for Large-Scale Text-to-SQL

Abstract: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 93.04% field-level strict recall rate, uses 116.55K average tokens, and improves downstream SQL generation under a fixed generator.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2605.29670 [cs.CL]

(or arXiv:2605.29670v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2605.29670 Focus to learn more arXiv-issued DOI via DataCite

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