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Existing Text-to-SQL generators require the entire schema to be encoded withthe user text. This is expensive or impractical for large databases with tensof thousands of columns. Standard dense retrieval techniques are inadequate forschema subsetting of a large structured database, where the correct semanticsof retrieval demands that we rank sets of schema elements rather thanindividual elements. In response, we propose a two-stage process for effectivecoverage during retrieval. First, we instruct an LLM to hallucinate a minimalDB schema deemed adequate to answer the query. We use the hallucinated schemato retrieve a subset of the actual schema, by composing the results frommultiple dense retrievals. Remarkably, hallucination $\unicode{x2013}$generally considered a nuisance $\unicode{x2013}$ turns out to be actuallyuseful as a bridging mechanism. Since no existing benchmarks exist for schemasubsetting on large databases, we introduce three benchmarks. Twosemi-synthetic datasets are derived from the union of schemas in two well-knowndatasets, SPIDER and BIRD, resulting in 4502 and 798 schema elementsrespectively. A real-life benchmark called SocialDB is sourced from an actuallarge data warehouse comprising 17844 schema elements. We show that our method1leads to significantly higher recall than SOTA retrieval-based augmentationmethods.

CRUSH4SQL: Collective Retrieval Using Schema Hallucination For Text2SQL