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In this work, we study multi-source test-time model adaptation from userfeedback, where K distinct models are established for adaptation. To allowefficient adaptation, we cast the problem as a stochastic decision-makingprocess, aiming to determine the best adapted model after adaptation. Wediscuss two frameworks: multi-armed bandit learning and multi-armed duelingbandits. Compared to multi-armed bandit learning, the dueling framework allowspairwise collaboration among K models, which is solved by a novel method namedCo-UCB proposed in this work. Experiments on six datasets of extractivequestion answering (QA) show that the dueling framework using Co-UCB is moreeffective than other strong baselines for our studied problem.

Multi-Source Test-Time Adaptation as Dueling Bandits for Extractive Question Answering