# Interpretation Gaps in LLM-Assisted Comprehension of Privacy Documents

arXiv: https://arxiv.org/abs/2503.12225 · HTML: https://mlsanity.com/paper/2503.12225/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 19 references that are in the map.
- First posted on arXiv 15 Mar 2025.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023)
- [A Comprehensive Overview of Large Language Models](https://mlsanity.com/paper/2307.06435/) (2023)
- [The Butterfly Effect of Altering Prompts: How Small Changes and Jailbreaks Affect Large Language Model Performance](https://mlsanity.com/paper/2401.03729/) (2024)
- [GoldCoin: Grounding Large Language Models in Privacy Laws via Contextual Integrity Theory](https://mlsanity.com/paper/2406.11149/) (2024)
- [On Context Utilization in Summarization with Large Language Models](https://mlsanity.com/paper/2310.10570/) (2023)
- [Large Language Models: A New Approach for Privacy Policy Analysis at Scale](https://mlsanity.com/paper/2405.20900/) (2024)
- [Rethinking Legal Compliance Automation: Opportunities with Large Language Models](https://mlsanity.com/paper/2404.14356/) (2024)
- [LegiLM: A Fine-Tuned Legal Language Model for Data Compliance](https://mlsanity.com/paper/2409.13721/) (2024)
- [Privacy Policy Analysis through Prompt Engineering for LLMs](https://mlsanity.com/paper/2409.14879/) (2024)

## Abstract (arXiv)

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.