  Prompt-based learning has shown its effectiveness in few-shot text
classification. One important factor in its success is a verbalizer, which
translates output from a language model into a predicted class. Notably, the
simplest and widely acknowledged verbalizer employs manual labels to represent
the classes. However, manual selection does not guarantee the optimality of the
selected words when conditioned on the chosen language model. Therefore, we
propose Label-Aware Automatic Verbalizer (LAAV), effectively augmenting the
manual labels to achieve better few-shot classification results. Specifically,
we use the manual labels along with the conjunction "and" to induce the model
to generate more effective words for the verbalizer. The experimental results
on five datasets across five languages demonstrate that LAAV significantly
outperforms existing verbalizers. Furthermore, our analysis reveals that LAAV
suggests more relevant words compared to similar approaches, especially in
mid-to-low resource languages.
