DAK-UCB: Diversity-Aware Prompt Routing for LLMs and Generative Models
The expansion of generative AI and LLM services underscores the growing need for adaptive mechanisms to select an appropriate available model to respond to a user's prompts. Recent works have proposed offline and online learning formulations to identify the optimal generative AI model for an input prompt, based solely on maximizing prompt-based fidelity evaluation scores, e.g., CLIP-Score in text-to-image generation. However, such fidelity-based selection methods overlook the diversity of generated outputs, and hence, they can fail to address potential diversity shortcomings in the generated responses. In this paper, we introduce the Diversity-Aware Kernelized Upper Confidence Bound (DAK-...
| Authors | Donya Jafari, Farzan Farnia |
|---|---|
| Primary category | cs.LG |
| Published | 24 March 2026 |
| Last updated at source | 25 March 2026 |
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