动态共享提示词，提升持续学习的效率与效果
Is Parameter Isolation Better for Prompt-Based Continual Learning?
- 构建全局提示池，通过门控路由动态激活提示
- 在多个数据集上显著优于固定提示分配方法
- 适合需要长期学习且资源受限的场景
基于提示的持续学习方法能有效缓解灾难性遗忘。然而，现有方法通常为每个任务分配固定的提示集，完全隔离各任务知识，导致参数利用不充分。为此，我们提出一种提示共享框架：构建全局提示池，并引入任务感知的门控路由机制，稀疏激活部分提示以实现任务特征表示的动态解耦与协同优化。此外，设计历史感知调制器，利用累积提示激活统计信息保护高频使用提示免受过度更新，从而减少参数浪费和知识遗忘。大量实验分析表明，该方法在有效性与效率上均显著优于现有静态分配策略。
原文摘要 · Abstract (English)
Prompt-based continual learning methods effectively mitigate catastrophic forgetting. However, most existing methods assign a fixed set of prompts to each task, completely isolating knowledge across tasks and resulting in suboptimal parameter utilization. To address this, we consider the practical needs of continual learning and propose a prompt-sharing framework. This framework constructs a global prompt pool and introduces a task-aware gated routing mechanism that sparsely activates a subset of prompts to achieve dynamic decoupling and collaborative optimization of task-specific feature representations. Furthermore, we introduce a history-aware modulator that leverages cumulative prompt activation statistics to protect frequently used prompts from excessive updates, thereby mitigating inefficient parameter usage and knowledge forgetting. Extensive analysis and empirical results demonstrate that our approach consistently outperforms existing static allocation strategies in effectiveness and efficiency.
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