SEEKR: Selective Attention-Guided Knowledge Retention for Continual Learning of Large Language Models

Understand

Continual learning (CL) is crucial for language models to dynamically adapt to the evolving real-world demands.

• To mitigate the catastrophic forgetting problem in CL, data replay has been proven a simple and effective strategy, and the subsequent data-replay-based distillation can further enhance the performance.
• However, existing methods fail to fully exploit the knowledge embedded in models from previous tasks, resulting in the need for a relatively large number of replay samples to achieve good results.
• In this work, we first explore and emphasize the importance of attention weights in knowledge retention, and then propose a SElective attEntion-guided Knowledge Retention method (SEEKR) for data-efficient replay-based continual learning of large language models (LLMs).