One Size Fits All for Semantic Shifts: Adaptive Prompt Tuning for
  Continual Learning
Doyoung Kim Susik Yoon Dongmin Park Youngjun Lee Hwanjun Song Jihwan Bang Jae-Gil Lee
In real-world continual learning scenarios, tasks often exhibit intricate and
unpredictable semantic shifts, posing challenges for fixed prompt management
strategies. We identify the inadequacy of universal and specific prompting in
handling these dynamic shifts. Universal prompting is ineffective for tasks
with abrupt semantic changes, while specific prompting struggles with
overfitting under mild semantic shifts. To overcome these limitations, we
propose an adaptive prompting approach that tailors minimal yet sufficient
prompts based on the task semantics. Our methodology, SemPrompt, incorporates a
two-level semantic grouping process: macroscopic semantic assignment and
microscopic semantic refinement. This process ensures optimal prompt
utilization for varying task semantics, improving the efficiency and
effectiveness of learning in real-world CL settings. Our experimental results
demonstrate that SemPrompt consistently outperforms existing methods in
adapting to diverse semantic shifts in tasks.