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New benchmark and adaptive embeddings boost SQL schema retrieval performance

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced a new benchmark and corpus-adaptive embeddings for SQL schema retrieval, a crucial step in text-to-SQL tasks that involves identifying relevant tables and columns within large databases. They adapted five existing text-to-SQL datasets to function as retrieval tasks and found that standard text and code embedders performed poorly. By fine-tuning a 305M-parameter embedder using synthesized queries and hard negatives, they significantly improved recall@10 from 60.4% to 75.6%, establishing schema linking as a distinct retrieval problem and demonstrating a practical method for its deployment at enterprise scale. AI

IMPACT Improves the efficiency and accuracy of text-to-SQL systems, potentially accelerating enterprise adoption of AI for data analysis.

RANK_REASON The cluster contains an academic paper detailing a new benchmark and methodology for SQL schema retrieval. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New benchmark and adaptive embeddings boost SQL schema retrieval performance

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The cluster contains an academic paper detailing a new benchmark and methodology for SQL schema retrieval. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Rajhans Samdani ·

    Finding the Right Tables and Columns: A Benchmark and Corpus-Adaptive Embeddings for SQL Schema Retrieval

    Retrieval in the SQL setting has largely been studied as the task of finding, within a large collection of SQL statements, the statement that answers a natural-language question. At scale, however, a more fundamental retrieval problem precedes generation: schema retrieval, identi…

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