AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Semantic query engines evaluate natural-language filters with a large language model, and every evaluation is a costly oracle call. Optimizers that reduce these calls face a dilemma: cascades that trust a cheap proxy miss their precision and recall targets on many queries, methods with finite-sample guarantees save lit...
SAGE achieves the strongest overall SemBench performance and, on a representative factorable join, reduces pairwise model calls by more than two orders of magnitude, yielding a 358-fold measured cost reduction.
This work demonstrates Text2SQL-Eval, an open-source, modular framework for rigorous evaluation of text-to-SQL systems, which provides a comprehensive suite of over twelve metrics, spanning execution accuracy, SQL syntactic equivalence, and LLM-as-judge scoring.
It is demonstrated that ORM-based verification provides a simple, effective, and scalable alternative to heuristic test-time selection strategies for Text-to-SQL, and that ORMs scale effectively with larger candidate sets and yield stronger improvements on complex queries.
As Large Language Models (LLMs) become foundational to next-generation Intelligent Information Systems, the bridge between natural language interfaces and structured database systems remains a critical bottleneck. While Text-to-SQL generation enables cooperative support for complex query formulation, ensuring the rel...
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