  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.
