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Semantic query engines evaluate natural-language filters with a large language model, and every evaluation is a costly oracle call. Optimizers that reduce these calls face a dilemma: cascades that trust a cheap proxy miss their precision and recall targets on many queries, methods with finite-sample guarantees save lit...
SAGE achieves the strongest overall SemBench performance and, on a representative factorable join, reduces pairwise model calls by more than two orders of magnitude, yielding a 358-fold measured cost reduction.
This work demonstrates Text2SQL-Eval, an open-source, modular framework for rigorous evaluation of text-to-SQL systems, which provides a comprehensive suite of over twelve metrics, spanning execution accuracy, SQL syntactic equivalence, and LLM-as-judge scoring.
TEmBed is introduced, the Tabular Embedding Test Bed, a unified benchmark for systematically evaluating tabular embeddings across four representation levels: cell, row, column, and table and shows that which model to use depends on the task and representation level.
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