  Multi-label few-shot aspect category detection aims at identifying multiple
aspect categories from sentences with a limited number of training instances.
The representation of sentences and categories is a key issue in this task.
Most of current methods extract keywords for the sentence representations and
the category representations. Sentences often contain many category-independent
words, which leads to suboptimal performance of keyword-based methods. Instead
of directly extracting keywords, we propose a label-guided prompt method to
represent sentences and categories. To be specific, we design label-specific
prompts to represent sentences by combining crucial contextual and semantic
information. Further, the label is introduced into a prompt to obtain category
descriptions by utilizing a large language model. This kind of category
descriptions contain the characteristics of the aspect categories, guiding the
construction of discriminative category prototypes. Experimental results on two
public datasets show that our method outperforms current state-of-the-art
methods with a 3.86% - 4.75% improvement in the Macro-F1 score.
