Recent works have shown promising results of prompt tuning in stimulating
pre-trained language models (PLMs) for natural language processing (NLP) tasks.
However, to the best of our knowledge, existing works focus on prompt-tuning
generative PLMs that are pre-trained to generate target tokens, such as BERT.
It is still unknown whether and how discriminative PLMs, e.g., ELECTRA, can be
effectively prompt-tuned. In this work, we present DPT, the first prompt tuning
framework for discriminative PLMs, which reformulates NLP tasks into a
discriminative language modeling problem. Comprehensive experiments on text
classification and question answering show that, compared with vanilla
fine-tuning, DPT achieves significantly higher performance, and also prevents
the unstable problem in tuning large PLMs in both full-set and low-resource
settings. The source code and experiment details of this paper can be obtained
from this https URL.

该论文提出了 DPT 作为针对区分性 PLMs 的 prompt tuning 框架，并将自然语言处理任务转换为区分性语言建模问题。通过全面的文本分类和问答实验表明，与 vanilla fine-tuning 相比，DPT 在全集和低资源环境下都能显著提高性能，并解决了调整大型 PLMs 中的不稳定问题。