ChatGPT, as a recently launched large language model (LLM), has shown superior performance in various natural language processing (NLP) tasks. However, two major limitations hinder its potential applications: (1) the inflexibility of finetuning on downstream tasks and (2) the lack of interpretability in the decision-making process. To tackle these limitations, we propose a novel framework that leverages the power of ChatGPT for specific tasks, such as text classification, while improving its interpretability. The proposed framework conducts a knowledge graph extraction task to extract refined and structural knowledge from the raw data using ChatGPT. The rich knowledge is then converted into a graph, which is further used to train an interpretable linear classifier to make predictions. To evaluate the effectiveness of our proposed method, we conduct experiments on four datasets. The result shows that our method can significantly improve the performance compared to directly utilizing ChatGPT for text classification tasks. And our method provides a more transparent decision-making process compared with previous text classification methods.

翻译：ChatGPT作为近期推出的大型语言模型，在各种自然语言处理任务中展现出卓越性能。然而，两大局限性制约了其潜在应用：（1）下游任务微调缺乏灵活性；（2）决策过程缺乏可解释性。为解决这些局限，我们提出了一种新颖框架，在利用ChatGPT处理文本分类等特定任务的同时提升其可解释性。该框架通过ChatGPT执行知识图谱抽取任务，从原始数据中提取精炼的结构化知识，随后将其转化为图结构，并用于训练可解释的线性分类器进行预测。为评估方法有效性，我们在四个数据集上开展实验。结果表明，相较于直接使用ChatGPT进行文本分类，本方法能显著提升性能，并且相比传统文本分类方法提供了更透明的决策过程。