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New diagnostic tool evaluates text-to-SQL models on schema context budgets

By PulseAugur Editorial · [1 sources] ·

Researchers have developed BudgetSchemaBench, a new diagnostic tool designed to evaluate how well text-to-SQL models handle large database schemas within limited context windows. The tool uses execution-grounded relevance labels derived from gold SQL queries to assess performance across various schema-context budgets and serialization methods. Findings indicate that increasing the schema budget significantly improves execution accuracy, particularly for lexical retrieval, while dense retrieval methods are less sensitive to budget changes. AI

IMPACT This diagnostic tool could help optimize how large language models process and utilize database schemas, potentially improving the efficiency and accuracy of data agents.

RANK_REASON The cluster contains an academic paper detailing a new diagnostic tool for text-to-SQL models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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

New diagnostic tool evaluates text-to-SQL models on schema context budgets

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The cluster contains an academic paper detailing a new diagnostic tool for text-to-SQL models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Chen Shen ·

    BudgetSchemaBench: A Budget-Swept Diagnostic for Schema Context in Text-to-SQL

    arXiv:2610.00092v1 Announce Type: cross Abstract: Data agents over structured sources must fit database schema into the model's context window. Large catalogs can span many databases and thousands of columns, so cost constraints may require choosing between table coverage and ser…

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