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: this https URL

这篇论文介绍了一种名为 Smart Expert System 的创新方法，利用大型语言模型作为文本分类器，简化了传统的文本分类工作流程，通过评估多个大型语言模型、机器学习算法和神经网络结构在四个数据集上的性能，证明了某些大型语言模型在情感分析、垃圾短信检测和多标签分类方面超过了传统方法，并且通过少样本学习和微调策略进一步提高了系统的性能，使得微调模型在所有数据集中表现最佳。