Building Efficient Universal Classifiers with Natural Language Inference

arXiv:2312.17543v2 Announce Type: replace
Abstract: Generative Large Language Models (LLMs) have become the mainstream choice for fewshot and zeroshot learning thanks to the universality of text generation. Many users, however, do not need the broad capabilities of generative LLMs when they only want to automate a classification task. Smaller BERT-like models can also learn universal tasks, which allow them to do any text classification task without requiring fine-tuning (zeroshot classification) or to learn new tasks with only a few examples …

abstract arxiv automate become bert building capabilities classification classifiers cs.ai cs.cl generative however inference language language models large language large language models llms natural natural language text text generation type universal