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This work studies adaptive routing of prompts to large language model experts to maximize response quality in an online setting with limited feedback and proposes algorithms that strategically select and observe rewards to minimize regret.
Forget single-shot QA: this paper introduces a new task of generating follow-up insights that extend and improve initial answers, enabling richer, more iterative user interactions.