Introduces the LLM ORDER BY semantic operator with algorithmic improvements, a semantic-aware external merge sort, and a budget-aware optimizer that selects near-optimal access paths for LLM-based ordering.
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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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Access Paths for Efficient Ordering with Large Language Models
Introduces the LLM ORDER BY semantic operator with algorithmic improvements, a semantic-aware external merge sort, and a budget-aware optimizer that selects near-optimal access paths for LLM-based ordering.
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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.