用对话式AI帮用户读懂隐私政策，理解更准、负担更轻。
Empowering Users in Digital Privacy Management through Interactive LLM-Based Agents
- 用大模型构建交互式助手，自动解析复杂隐私条款。
- 用户理解力提升至2.6分（满分3分），任务耗时减少至5.5分钟。
- 适合普通用户、隐私保护研究者，降低法律文本认知门槛。
本文提出一种基于大语言模型（LLM）的交互式对话代理，用于提升用户对隐私政策的理解。实验表明，该模型在数据实践识别、选项识别、政策摘要和隐私问答等任务上显著优于传统方法，达到新基准。基于此，我们设计了一个无需用户主动提问即可引导理解网站隐私政策的智能代理。100名参与者的对照实验显示，使用代理的用户平均理解得分达2.6（满分3），远高于对照组的1.8；任务难度评分从7.8降至3.2（满分10）；完成时间由15.8分钟缩短至5.5分钟；同时用户对隐私管理的信心明显增强。本工作展示了大模型代理在提升用户数字隐私自主权方面的巨大潜力。
原文摘要 · Abstract (English)
This paper presents a novel application of large language models (LLMs) to enhance user comprehension of privacy policies through an interactive dialogue agent. We demonstrate that LLMs significantly outperform traditional models in tasks like Data Practice Identification, Choice Identification, Policy Summarization, and Privacy Question Answering, setting new benchmarks in privacy policy analysis. Building on these findings, we introduce an innovative LLM-based agent that functions as an expert system for processing website privacy policies, guiding users through complex legal language without requiring them to pose specific questions. A user study with 100 participants showed that users assisted by the agent had higher comprehension levels (mean score of 2.6 out of 3 vs. 1.8 in the control group), reduced cognitive load (task difficulty ratings of 3.2 out of 10 vs. 7.8), increased confidence in managing privacy, and completed tasks in less time (5.5 minutes vs. 15.8 minutes). This work highlights the potential of LLM-based agents to transform user interaction with privacy policies, leading to more informed consent and empowering users in the digital services landscape.
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