ChatGraph: Interpretable Text Classification by Converting ChatGPT Knowledge to Graphs
This work addresses the problem of inflexibility and lack of interpretability in ChatGPT for text classification, offering a more transparent method for NLP practitioners, though it is incremental as it builds on existing LLM and graph-based approaches.
The paper tackles the limitations of ChatGPT in text classification by proposing a framework that extracts knowledge from ChatGPT to build graphs, which are then used to train an interpretable linear classifier, resulting in significant performance improvements over directly using ChatGPT on four datasets.
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.