  Text classification is a fundamental task in Natural Language Processing
(NLP), and the advent of Large Language Models (LLMs) has revolutionized the
field. This paper introduces the Smart Expert System, a novel approach that
leverages LLMs as text classifiers. The system simplifies the traditional text
classification workflow, eliminating the need for extensive preprocessing and
domain expertise. The performance of several LLMs, machine learning (ML)
algorithms, and neural network (NN) based structures is evaluated on four
datasets. Results demonstrate that certain LLMs surpass traditional methods in
sentiment analysis, spam SMS detection and multi-label classification.
Furthermore, it is shown that the system's performance can be further enhanced
through few-shot or fine-tuning strategies, making the fine-tuned model the top
performer across all datasets. Source code and datasets are available in this
GitHub repository: https://github.com/yeyimilk/llm-zero-shot-classifiers.
