Prototypical Verbalizer for Prompt-based Few-shot Tuning

Understand

Prompt-based tuning for pre-trained language models (PLMs) has shown its effectiveness in few-shot learning.

• Typically, prompt-based tuning wraps the input text into a cloze question.
• To make predictions, the model maps the output words to labels via a verbalizer, which is either manually designed or automatically built.
• However, manual verbalizers heavily depend on domain-specific prior knowledge and human efforts, while finding appropriate label words automatically still remains challenging.In this work, we propose the prototypical verbalizer (ProtoVerb) which is built directly from training data.