  Conversational NLU providers often need to scale to thousands of
intent-classification models where new customers often face the cold-start
problem. Scaling to so many customers puts a constraint on storage space as
well. In this paper, we explore four different zero and few-shot intent
classification approaches with this low-resource constraint: 1) domain
adaptation, 2) data augmentation, 3) zero-shot intent classification using
descriptions large language models (LLMs), and 4) parameter-efficient
fine-tuning of instruction-finetuned language models. Our results show that all
these approaches are effective to different degrees in low-resource settings.
Parameter-efficient fine-tuning using T-few recipe (Liu et al., 2022) on
Flan-T5 (Chang et al., 2022) yields the best performance even with just one
sample per intent. We also show that the zero-shot method of prompting LLMs
using intent descriptions
