Research on Policy Tools Classification Based on ChatGPT Augmentation and Supervised Contrastive Learning

被引：0
|
作者：
Hu, Zhiqiang [1 ]
Li, Pengjun [1 ]
Wang, Jinlong [1 ]
Xiong, Xiaoyun [1 ]
机构：
[1] School of Information and Control Engineering, Qingdao University of Technology, Shandong, Qingdao,266525, China
来源：
Computer Engineering and Applications | 2024年 / 60卷 / 07期
关键词：
The classification of policy tools is an important dimension in the quantification and analysis of policy texts. Due to the scarcity of training data; models are prone to overfitting; resulting in reduced prediction confidence and an increased risk of misclassification. Therefore; policy tool classification method based on ChatGPT augmentation and supervised contrastive learning is proposed. The method consists of two stages: pre-training language model fine-tuning and ChatGPT decision augmentation. In the first stage; ChatGPT; a large language model; augments policy texts to increase the training dataset; while supervised contrastive learning fine-tune the RoBERTa model to improve classification performance. In the second stage; ChatGPT assists in the decision-making process for low confidence texts through the pretrained language model and reduces the risk of misclassifying similar texts. Experiments on the digital industry policy tools classification dataset and the Tnews dataset show that the proposed method surpasses mainstream research approaches and can effectively improve the performance of the base model; with a more significant improvement observed when the training samples are limited. © 2024 Journal of Computer Engineering and Applications Beijing Co; Ltd; Science Press. All rights reserved;
D O I：
10.3778/j.issn.1002-8331.2308-0354
中图分类号：
学科分类号：
摘要：
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页码：292 / 305
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