Eliciting Knowledge from Pretrained Language Models for Prototypical Prompt Verbalizer. (arXiv:2201.05411v1 [cs.CL])

Recent advances on prompt-tuning cast few-shot classification tasks as a
masked language modeling problem. By wrapping input into a template and using a
verbalizer which constructs a mapping between label space and label word space,
prompt-tuning can achieve excellent results in zero-shot and few-shot
scenarios. However, typical prompt-tuning needs a manually designed verbalizer
which requires domain expertise and human efforts. And the insufficient label
space may introduce considerable bias into the results. In this paper, we focus
on eliciting knowledge …

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