arXiv AI
The Stochastic Shift: A New Evaluation Paradigm for Text-to-SQL with AI Operators
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Jul 8
Spider 2.0-AIFunc: Extending Real-World Text-to-SQL to AI-Native SQL Workflows
arXiv:2607. 06229v1 Announce Type: cross Abstract: Major cloud data platforms now expose large language model capabilities as native SQL functions, enabling analysts to perform classification, filtering, sentiment analysis, extraction, similarity search, and aggregation within ordinary SQL queries.
arXiv AI
Sep 10
SQLMorph: Query Mutation and Fine-Grained Metrics for Text-to-SQL Evaluation
arXiv:2609.08950v1 Announce Type: cross Abstract: Text-to-SQL systems translate natural language queries into executable SQL, democratizing access to structured data. Despite recent advances driven b...
arXiv AI
Aug 18
ACTS-SQL: Agentic and Critic-Oriented Tree-Structured SQL Correctness with Large Language Models
arXiv:2608. 15145v1 Announce Type: new Abstract: Large Language Models (LLMs) have been increasingly adopted in Text-to-SQL systems, yet SQL errors remain a major obstacle in real-world Text-to-SQL inference pipelines.
arXiv AI
Jun 9
UniQL: Towards Dialect-Universal Benchmarking for Text-to-SQL
arXiv:2606. 08018v1 Announce Type: new Abstract: Existing text-to-SQL benchmarks are largely centered on SQLite, making it difficult to evaluate whether models can generalize across heterogeneous SQL dialects.
arXiv AI
Aug 5
Evaluating LLMs in Database Scenarios: A Lifecycle Benchmark for Assessing Their Potential in Core Database Tasks
arXiv:2608. 03794v1 Announce Type: cross Abstract: Large Language Models (LLMs) are transforming database interaction paradigms, evolving from simple query translators to autonomous database administrators (DBAs).
arXiv AI
Aug 24
SAGE: A Unified Algebra and Self-Adaptive Execution for AI Functions in SQL
SAGE (Self-Adaptive Generative Execution) introduces a unified framework for integrating AI functions into SQL by defining three typed primitives—AI_SCALAR, AI_AGG, and AI_JOIN—that correspond to the relational roles of transforming rows, aggregating groups, and joining row pairs. The framework standardizes a confidence-gated execution interface and tailors physical strategies to each primitive’s shape, with AI_JOIN employing predicate analysis and a recipe card to select optimal execution plans. Evaluations across scalar, aggregate, and join workloads demonstrate that SAGE consistently improves execution quality and efficiency, achieving the best overall SemBench performance and dramatically reducing model calls in factorable joins.