Summary
This research introduces a NeuralUCB-based policy for cost-aware routing of large language models (LLMs), aiming to improve efficiency and adaptivity over existing methods. Evaluated on RouterBench in a simulated online setting, the proposed technique consistently demonstrated superior utility compared to random and min-cost baselines. This advancement offers a more effective approach to managing LLM deployment costs and performance.
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