  Prompt tuning has been an extremely effective tool to adapt a pre-trained
model to downstream tasks. However, standard prompt-based methods mainly
consider the case of sufficient data of downstream tasks. It is still unclear
whether the advantage can be transferred to the few-shot regime, where only
limited data are available for each downstream task. Although some works have
demonstrated the potential of prompt-tuning under the few-shot setting, the
main stream methods via searching discrete prompts or tuning soft prompts with
limited data are still very challenging. Through extensive empirical studies,
we find that there is still a gap between prompt tuning and fully fine-tuning
for few-shot learning. To bridge the gap, we propose a new prompt-tuning
framework, called Soft Template Tuning (STT). STT combines manual and auto
prompts, and treats downstream classification tasks as a masked language
modeling task. Comprehensive evaluation on different settings suggests STT can
close the gap between fine-tuning and prompt-based methods without introducing
additional parameters. Significantly, it can even outperform the time- and
resource-consuming fine-tuning method on sentiment classification tasks.
