  Prompting methods have shown impressive performance in a variety of text
mining tasks and applications, especially few-shot ones. Despite the promising
prospects, the performance of prompting model largely depends on the design of
prompt template and verbalizer. In this work, we propose MetricPrompt, which
eases verbalizer design difficulty by reformulating few-shot text
classification task into text pair relevance estimation task. MetricPrompt
adopts prompting model as the relevance metric, further bridging the gap
between Pre-trained Language Model's (PLM) pre-training objective and text
classification task, making possible PLM's smooth adaption. Taking a training
sample and a query one simultaneously, MetricPrompt captures cross-sample
relevance information for accurate relevance estimation. We conduct experiments
on three widely used text classification datasets across four few-shot
settings. Results show that MetricPrompt outperforms manual verbalizer and
other automatic verbalizer design methods across all few-shot settings,
achieving new state-of-the-art (SOTA) performance.
