The number and dynamic nature of web and mobile applications presents
significant challenges for assessing their compliance with data protection
laws. In this context, symbolic and statistical Natural Language Processing
(NLP) techniques have been employed for the automated analysis of these
systems' privacy policies. However, these techniques typically require
labor-intensive and potentially error-prone manually annotated datasets for
training and validation. This research proposes the application of Large
Language Models (LLMs) as an alternative for effectively and efficiently
extracting privacy practices from privacy policies at scale. Particularly, we
leverage well-known LLMs such as ChatGPT and Llama 2, and offer guidance on the
optimal design of prompts, parameters, and models, incorporating advanced
strategies such as few-shot learning. We further illustrate its capability to
detect detailed and varied privacy practices accurately. Using several renowned
datasets in the domain as a benchmark, our evaluation validates its exceptional
performance, achieving an F1 score exceeding 93%. Besides, it does so with
reduced costs, faster processing times, and fewer technical knowledge
requirements. Consequently, we advocate for LLM-based solutions as a sound
alternative to traditional NLP techniques for the automated analysis of privacy
policies at scale.

利用大型语言模型（LLM）以替代传统的自然语言处理技术，在较大规模上从隐私政策中高效准确地提取隐私做法，并实现出色的性能评估和降低成本。

大型语言模型：大规模隐私政策分析的新方法

Large Language Models: A New Approach for Privacy Policy Analysis at  Scale

We study web and mobile applications that are used to schedule advance
service, from medical appointments to restaurant reservations. We model them as
online weighted bipartite matching problems with non-stationary arrivals. We
propose new algorithms with performance guarantees for this class of problems.
Specifically, we show that the expected performance of our algorithms is
bounded below by $1-\sqrt{\frac{2}{\pi}}\frac{1}{\sqrt{k}}+O(\frac{1}{k})$
times that of an optimal offline algorithm, which knows all future information
upfront, where $k$ is the minimum capacity of a resource. This is the tightest
known lower bound. This performance analysis holds for any Poisson arrival
process. Our algorithms can also be applied to a number of related problems,
including display ad allocation problems and revenue management problems for
opaque products. We test the empirical performance of our algorithms against
several well-known heuristics by using appointment scheduling data from a major
academic hospital system in New York City. The results show that the algorithms
exhibit the best performance among all the tested policies. In particular, our
algorithms are $21\%$ more effective than the actual scheduling strategy used
in the hospital system according to our performance metric.

本文研究预约服务的 Web 和移动应用程序，针对该类问题提出了带性能保证的新算法，并在纽约市卫生系统的预约数据上进行了测试。