Privacy policies serve as the primary conduit through which online service
providers inform users about their data collection and usage procedures.
However, in a bid to be comprehensive and mitigate legal risks, these policy
documents are often quite verbose. In practical use, users tend to click the
Agree button directly rather than reading them carefully. This practice exposes
users to risks of privacy leakage and legal issues. Recently, the advent of
Large Language Models (LLM) such as ChatGPT and GPT-4 has opened new
possibilities for text analysis, especially for lengthy documents like privacy
policies. In this study, we investigate a privacy policy text analysis
framework PolicyGPT based on the LLM. This framework was tested using two
datasets. The first dataset comprises of privacy policies from 115 websites,
which were meticulously annotated by legal experts, categorizing each segment
into one of 10 classes. The second dataset consists of privacy policies from
304 popular mobile applications, with each sentence manually annotated and
classified into one of another 10 categories. Under zero-shot learning
conditions, PolicyGPT demonstrated robust performance. For the first dataset,
it achieved an accuracy rate of 97%, while for the second dataset, it attained
an 87% accuracy rate, surpassing that of the baseline machine learning and
neural network models.

通过利用大型语言模型（LLM）开展文本分析，本研究构建了 PolicyGPT 隐私政策文本分析框架，并在两个数据集上进行了测试，结果表明在零样本学习条件下，PolicyGPT 取得了强大的性能，并优于传统机器学习和神经网络模型。