Towards Optimizing SQL Generation via LLM Routing

The article introduces a novel language model routing approach for Text-to-SQL, a system that allows users to interact with databases through natural language. The new approach dynamically selects the most cost-effective large language model (LLM) that can generate accurate SQL for each query. This method is designed to reduce unnecessary latency and cost for simpler queries, while maintaining high accuracy for complex ones.

Two routing strategies, score-based and classification-based, are presented in the paper. These strategies are designed for easy training and efficient inference, and they achieve accuracy comparable to the most capable LLM while reducing costs. The authors highlight a practical and explainable accuracy-cost trade-off using the BIRD dataset in their experiments.

Key takeaways:

The paper introduces the first large language model (LLM) routing approach for Text-to-SQL, which selects the most cost-effective LLM for each query.
Two routing strategies are presented, score- and classification-based, that achieve accuracy comparable to the most capable LLM while reducing costs.
The routers are designed for ease of training and efficient inference.
The experiments highlight a practical and explainable accuracy-cost trade-off on the BIRD dataset.

Towards Optimizing SQL Generation via LLM Routing

Key takeaways:

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