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.

利用 Progressive Prompts 方法解决语言模型领域中的连续学习问题，它以前向传递为基础，无需数据重放或大量的任务特定参数，并且其推动学习的方式有助于抵御灾难性遗忘，实验表明相对于 T5 模型的最佳方法，平均测试准确性提高了 20% 以上，即使在较长序列的任务中，我们的方法也明显优于之前的方法。