Large Language Models Are Zero-Shot Text Classifiers
Zhiqiang Wang Yiran Pang Yanbin Lin
Retrained large language models (LLMs) have become extensively used across
various sub-disciplines of natural language processing (NLP). In NLP, text
classification problems have garnered considerable focus, but still faced with
some limitations related to expensive computational cost, time consumption, and
robust performance to unseen classes. With the proposal of chain of thought
prompting (CoT), LLMs can be implemented using zero-shot learning (ZSL) with
the step by step reasoning prompts, instead of conventional question and answer
formats. The zero-shot LLMs in the text classification problems can alleviate
these limitations by directly utilizing pretrained models to predict both seen
and unseen classes. Our research primarily validates the capability of GPT
models in text classification. We focus on effectively utilizing prompt
strategies to various text classification scenarios. Besides, we compare the
performance of zero shot LLMs with other state of the art text classification
methods, including traditional machine learning methods, deep learning methods,
and ZSL methods. Experimental results demonstrate that the performance of LLMs
underscores their effectiveness as zero-shot text classifiers in three of the
four datasets analyzed. The proficiency is especially advantageous for small
businesses or teams that may not have extensive knowledge in text
classification.