Title: UiTTa: Online Test-Time Adaptation by User Interaction. Abstract: We explore user interaction-based test-time adaptation (UITTA), which adapts a model to shifted test distributions with supervision signals from model-user interactions. Model adaptation in TTA can fail since models learn from the noisy pseudo-labels of the test data. UITTA achieves better adaptation from user feedback on top-K predictions within two rounds of simulated interactions. To have real-time adaptation, we further accelerate model optimization by reducing the cost of gradient backpropagation, through random dropping of backward paths. Simulation experiments on cross-lingual transfer, domain generalization, and corruption robustness show that low-cost user feedback can significantly boost TTA in performance, even competing with online active learning which however needs expensive human annotation. By accelerating pre-trained language models, we reduce 70% – 90% backpropagation cost with only a small drop in performance.