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 https://github.com/thunlp/DPT.

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。

针对具有鉴别力的预训练语言模型的提示调节