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

本文探讨了在低资源环境下使用领域适应、数据增强、零样本分类以及参数高效的微调等四种方法来解决对话 NLU 提供商在扩展到数千个意图分类模型时遇到的存储空间限制问题，结果表明这些方法对于不同程度的低资金环境下效果有效，在使用 T-few 配方提出的参数高效昆虫微调语言模型的 Flan-T5 上获得了最佳性能，即使只有一个样本每意图。我们还展示了使用意图描述促进 LLM 的零样本方法。