Multi-Source Test-Time Adaptation as Dueling Bandits for Extractive
  Question Answering
Hai Ye Qizhe Xie Hwee Tou Ng
In this work, we study multi-source test-time model adaptation from user
feedback, where K distinct models are established for adaptation. To allow
efficient adaptation, we cast the problem as a stochastic decision-making
process, aiming to determine the best adapted model after adaptation. We
discuss two frameworks: multi-armed bandit learning and multi-armed dueling
bandits. Compared to multi-armed bandit learning, the dueling framework allows
pairwise collaboration among K models, which is solved by a novel method named
Co-UCB proposed in this work. Experiments on six datasets of extractive
question answering (QA) show that the dueling framework using Co-UCB is more
effective than other strong baselines for our studied problem.