STEF is a schema-agnostic evaluation framework that scores SQL generation accuracy from natural language inputs using semantic feature alignment and a composite metric.
Victor Zhong, Caiming Xiong, and Richard Socher
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.AI 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
BADGER is a new enterprise evaluation framework that adds LLM-assisted SQL component extraction and a Hybrid-EX metric validated on 150 human-annotated queries to existing text-to-SQL and agentic assessment methods.
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Agent-Agnostic Evaluation of SQL Accuracy in Production Text-to-SQL Systems
STEF is a schema-agnostic evaluation framework that scores SQL generation accuracy from natural language inputs using semantic feature alignment and a composite metric.
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BADGER: Bridging Agentic and Deterministic Evaluation for Generative Enterprise Reasoning
BADGER is a new enterprise evaluation framework that adds LLM-assisted SQL component extraction and a Hybrid-EX metric validated on 150 human-annotated queries to existing text-to-SQL and agentic assessment methods.