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.

本研究提出了一种新的框架，利用 ChatGPT 进行文本分类等特定任务，同时提高其解释性。该框架通过知识图谱抽取任务从原始数据中提取精细和结构化的知识，并将其转化为图形，进而用于训练可解释的线性分类器进行预测。实验结果表明，与直接使用 ChatGPT 相比，我们的方法可以显著提高性能，并提供比以前文本分类方法更透明的决策过程。