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This paper proposes algorithms for adaptively routing prompts to LLM experts in an online setting with limited feedback, formulated as a bandit problem to minimize regret and maximize response quality.
This paper formulates the adaptive routing of prompts to large language model experts as a contextual bandit problem with limited feedback, proposing algorithms that achieve sublinear regret and demonstrate efficient learning of high-quality routing strategies.