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.
@article{arxiv.2108.13161,
title = {Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners},
author = {Ningyu Zhang and Luoqiu Li and Xiang Chen and Shumin Deng and Zhen Bi and Chuanqi Tan and Fei Huang and Huajun Chen},
journal= {arXiv preprint arXiv:2108.13161},
year = {2023}
}