One Size Fits All for Semantic Shifts: Adaptive Prompt Tuning for Continual Learning

摘要

In real-world continual learning (CL) scenarios, tasks often exhibit intricate and unpredictable semantic shifts, posing challenges for *fixed* prompt management strategies which are tailored to only handle semantic shifts of *uniform* degree (i.e., uniformly mild or uniformly abrupt). To address this limitation, we propose an *adaptive* prompting approach that effectively accommodates semantic shifts of *varying* degree where mild and abrupt shifts are mixed. AdaPromptCL employs the assign-and-refine semantic grouping mechanism that dynamically manages prompt groups in accordance with the semantic similarity between tasks, enhancing the quality of grouping through continuous refinement. Our experiment results demonstrate that AdaPromptCL outperforms existing prompting methods by up to 21.3%, especially in the benchmark datasets with diverse semantic shifts between tasks.