用能量模型生成旧任务样本，缓解大模型学新任务时遗忘旧知识的问题。
PPSEBM: An Energy-Based Model with Progressive Parameter Selection for Continual Learning
- 通过渐进式参数选择为每项新任务分配专属参数。
- 能量模型生成历史任务的伪样本以辅助记忆保留。
- 适合需要长期学习新任务又不丢旧知识的NLP应用。
持续学习在机器学习中仍是核心挑战，要求模型能从任务流中学习而不遗忘已有知识。主要障碍是灾难性遗忘，即在学习新任务时旧任务性能下降。本文提出PPSEBM框架，将能量模型（EBM）与渐进式参数选择（PPS）结合，有效缓解自然语言处理中的灾难性遗忘。在该框架中，渐进式参数选择为每个新任务分配独立的任务专属参数，而能量模型则从先前任务中生成代表性伪样本。这些伪样本主动参与并引导参数选择过程，增强模型对历史知识的保留能力，同时适应新任务。在多个NLP基准上的实验表明，PPSEBM优于当前最先进的持续学习方法，提供了一种有前景且稳健的解决方案。
原文摘要 · Abstract (English)
Continual learning remains a fundamental challenge in machine learning, requiring models to learn from a stream of tasks without forgetting previously acquired knowledge. A major obstacle in this setting is catastrophic forgetting, where performance on earlier tasks degrades as new tasks are learned. In this paper, we introduce PPSEBM, a novel framework that integrates an Energy-Based Model (EBM) with Progressive Parameter Selection (PPS) to effectively address catastrophic forgetting in continual learning for natural language processing tasks. In PPSEBM, progressive parameter selection allocates distinct, task-specific parameters for each new task, while the EBM generates representative pseudo-samples from prior tasks. These generated samples actively inform and guide the parameter selection process, enhancing the model's ability to retain past knowledge while adapting to new tasks. Experimental results on diverse NLP benchmarks demonstrate that PPSEBM outperforms state-of-the-art continual learning methods, offering a promising and robust solution to mitigate catastrophic forgetting.
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