用大模型帮用户读懂隐私政策，支持快速概览与深入对话。
Helping Johnny Make Sense of Privacy Policies with LLMs
- 结合大模型与检索增强生成，提供可交互的政策解析界面。
- 22名用户参与研究，普遍认可清晰概览与对话式解释的价值。
- 强调需解决幻觉与对抗鲁棒性问题，适合隐私工具开发者参考。
理解并参与隐私政策对在线隐私至关重要，但这些文件仍以复杂难读著称。我们提出PRISMe，一款交互式浏览器插件，融合大模型驱动的政策评估、仪表盘和可定制聊天界面，使用户在浏览时能快速获取概览或深入探究细节。我们开展了一项包含22名不同隐私知识背景用户的用户研究，探讨用户如何理解工具的解释及其对隐私政策参与度的影响，识别出不同的交互模式。参与者赞赏清晰概览和对话深度，但指出存在对抗鲁棒性和幻觉风险。因此，我们进一步通过检索增强生成（RAG）方法重新运行研究中的聊天查询，以缓解这些问题。研究揭示了设计挑战与技术权衡，为未来以用户为中心、值得信赖的隐私政策分析工具提供了可操作的见解。
原文摘要 · Abstract (English)
Understanding and engaging with privacy policies is crucial for online privacy, yet these documents remain notoriously complex and difficult to navigate. We present PRISMe, an interactive browser extension that combines LLM-based policy assessment with a dashboard and customizable chat interface, enabling users to skim quick overviews or explore policy details in depth while browsing. We conduct a user study (N=22) with participants of diverse privacy knowledge to investigate how users interpret the tool's explanations and how it shapes their engagement with privacy policies, identifying distinct interaction patterns. Participants valued the clear overviews and conversational depth, but flagged some issues, particularly adversarial robustness and hallucination risks. Thus, we investigate how a retrieval-augmented generation (RAG) approach can alleviate issues by re-running the chat queries from the study. Our findings surface design challenges as well as technical trade-offs, contributing actionable insights for developing future user-centered, trustworthy privacy policy analysis tools.
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