Empowering Users in Digital Privacy Management through Interactive LLM-Based Agents

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 LLMbased agents to transform user interaction with privacy policies, leading to more informed consent and empowering users in the digital services landscape. Recent advances in natural language processing (NLP), particularly through the development of large language models (LLMs), offer promising solutions to these challenges. LLMs, such as those based on GPT architectures, have demonstrated remarkable capabilities in understanding and generating human-like text across various domains, including legal documents. These models have been successfully applied to tasks such as information extraction, content summarization, and question