Towards Optimizing SQL Generation via LLM Routing

• URL: http://arxiv.org/abs/2411.04319v1
• Date: Wed, 06 Nov 2024 23:47:54 GMT
• Title: Towards Optimizing SQL Generation via LLM Routing
• Authors: Mohammadhossein Malekpour, Nour Shaheen, Foutse Khomh, Amine Mhedhbi,
• Abstract summary: Large language models (LLMs) achieve strong accuracy for complex queries, but incur unnecessary latency and dollar cost for simpler ones. We introduce the first LLM routing approach for Text-to-sql, which dynamically selects the most cost-effective LLM for each query. We present two routing strategies that achieve accuracy comparable to the most capable LLM while reducing costs.
• Score: 10.586036551269935
• License:
• Abstract: Text-to-SQL enables users to interact with databases through natural language, simplifying access to structured data. Although highly capable large language models (LLMs) achieve strong accuracy for complex queries, they incur unnecessary latency and dollar cost for simpler ones. In this paper, we introduce the first LLM routing approach for Text-to-SQL, which dynamically selects the most cost-effective LLM capable of generating accurate SQL for each query. We present two routing strategies (score- and classification-based) that achieve accuracy comparable to the most capable LLM while reducing costs. We design the routers for ease of training and efficient inference. In our experiments, we highlight a practical and explainable accuracy-cost trade-off on the BIRD dataset.

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