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.

我们提出了一种自适应提示方法，通过宏观语义分配和微观语义细化的过程，根据任务语义量身定制最小且足够的提示，以提高在真实连续学习环境中学习的效率和效果。实验证明，该方法在适应各种任务中的不同语义转变方面始终优于现有方法。