  This paper introduces \textbf{Q-tuning}, a novel approach for continual
prompt tuning that enables the lifelong learning of a pre-trained language
model. When learning a new task, Q-tuning trains a task-specific prompt by
adding it to a prompt queue consisting of the prompts from older tasks. To
better transfer the knowledge of old tasks, we design an adaptive knowledge
aggregation technique that reweighs previous prompts in the queue with a
learnable low-rank matrix. Once the prompt queue reaches its maximum capacity,
we leverage a PCA-based eviction rule to reduce the queue's size, allowing the
newly trained prompt to be added while preserving the primary knowledge of old
tasks. In order to mitigate the accumulation of information loss caused by the
eviction, we additionally propose a globally shared prefix prompt and a memory
retention regularization based on information theory. Extensive experiments
demonstrate that our approach outperforms the state-of-the-art methods
substantially on continual prompt tuning benchmarks. Moreover, our approach
enables lifelong learning on linearly growing task sequences while requiring
constant complexity for training and inference.
