Interpretation Gaps in LLM-Assisted Comprehension of Privacy Documents
This addresses privacy management challenges for users and organizations, but is incremental as it identifies problems rather than solving them.
The paper investigates accuracy, completeness, clarity, and representation gaps that occur when using large language models to simplify complex privacy policies, highlighting issues without providing specific numerical results.
This article explores the gaps that can manifest when using a large language model (LLM) to obtain simplified interpretations of data practices from a complex privacy policy. We exemplify these gaps to showcase issues in accuracy, completeness, clarity and representation, while advocating for continued research to realize an LLM's true potential in revolutionizing privacy management through personal assistants and automated compliance checking.