  Transfer learning from pretrained language models recently became the
dominant approach for solving many NLP tasks. A common approach to transfer
learning for multiple tasks that maximize parameter sharing trains one or more
task-specific layers on top of the language model. In this paper, we present an
alternative approach based on adversarial reprogramming, which extends earlier
work on automatic prompt generation. Adversarial reprogramming attempts to
learn task-specific word embeddings that, when concatenated to the input text,
instruct the language model to solve the specified task. Using up to 25K
trainable parameters per task, this approach outperforms all existing methods
with up to 25M trainable parameters on the public leaderboard of the GLUE
benchmark. Our method, initialized with task-specific human-readable prompts,
also works in a few-shot setting, outperforming GPT-3 on two SuperGLUE tasks
with just 32 training samples.
