  Large-scale pre-trained language models have contributed significantly to
natural language processing by demonstrating remarkable abilities as few-shot
learners. However, their effectiveness depends mainly on scaling the model
parameters and prompt design, hindering their implementation in most real-world
applications. This study proposes a novel pluggable, extensible, and efficient
approach named DifferentiAble pRompT (DART), which can convert small language
models into better few-shot learners without any prompt engineering. The main
principle behind this approach involves reformulating potential natural
language processing tasks into the task of a pre-trained language model and
differentially optimizing the prompt template as well as the target label with
backpropagation. Furthermore, the proposed approach can be: (i) Plugged to any
pre-trained language models; (ii) Extended to widespread classification tasks.
A comprehensive evaluation of standard NLP tasks demonstrates that the proposed
approach achieves a better few-shot performance. Code is available in
https://github.com/zjunlp/DART.
