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A guide on evaluating speech-to-text APIs by ranking transcript mistakes based on their actual impact on users, rather than raw accuracy metrics.
This paper introduces an online contextual Pandora's Box model for adaptively querying and selecting LLM APIs, proposing a learning approach that combines GMM estimation with UCB-style confidence bounds and proving dimension-dependent regret bounds.