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This paper introduces a novel measure called relative parameter importance for task-agnostic, replay-free continual learning, enabling better balance between stability and plasticity by regularizing only parameters critical for past tasks while allowing others to update for backward knowledge transfer. The method is evaluated on class-incremental and domain-incremental text classification tasks.
本文提出了一种面向大型语言模型的归因引导持续微调框架，该框架能够估计 Transformer 层中特定任务相关的参数重要性并相应地调节梯度，在保持新任务性能的同时缓解了灾难性遗忘。