We introduce Progressive Prompts - a simple and efficient approach for continual learning in language models. Our method allows forward transfer and resists catastrophic forgetting, without relying on data replay or a large number of task-specific parameters. Progressive Prompts learns a new soft prompt for each task and sequentially concatenates it with the previously learned prompts, while keeping the base model frozen. Experiments on standard continual learning benchmarks show that our approach outperforms state-of-the-art methods, with an improvement >20% in average test accuracy over the previous best-preforming method on T5 model. We also explore a more challenging continual learning setup with longer sequences of tasks and show that Progressive Prompts significantly outperforms prior methods.

翻译：我们提出渐进式提示（Progressive Prompts）——一种简单高效的语言模型持续学习方法。该方法能够实现前向迁移并抵抗灾难性遗忘，无需依赖数据回放或大量任务特定参数。渐进式提示为每个任务学习一个新的软提示，并将其与先前学到的提示顺序拼接，同时保持基础模型冻结。在标准持续学习基准上的实验表明，我们的方法优于现有最优方法，在T5模型上的平均测试准确率比先前最佳方法提升超过20%。我们还探索了更具挑战性的长任务序列持续学习设置，结果显示渐进式提示显著优于先前方法。