Summary of Entailment-driven Privacy Policy Classification with Llms, by Bhanuka Silva et al.
Entailment-Driven Privacy Policy Classification with LLMs
by Bhanuka Silva, Dishanika Denipitiyage, Suranga Seneviratne, Anirban Mahanti, Aruna Seneviratne
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel Large Language Model (LLM) based framework is proposed to classify paragraphs of privacy policies into easily understood labels for users, outperforming traditional LLM methods with an improved F1 score by 11.2%. The framework utilizes entailment-driven approaches to provide inherently explainable and meaningful predictions, addressing the issue of lengthy and complicated privacy policies that often lead to uninformed consent. This work has the potential to develop more effective tools for parsing privacy policies and helping users make informed decisions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Privacy policies can be overwhelming and hard to understand, leading many people to not read them at all. New ways are being explored to make these policies more user-friendly. One idea is to use Large Language Models (LLMs) to help people understand what’s going on in these policies. In this paper, researchers propose a new way to do this using entailment-driven LLMs. This approach does better than other methods and can explain its results in a clear way. This could lead to more effective tools for helping users make informed decisions about their privacy. |
Keywords
» Artificial intelligence » F1 score » Large language model » Parsing
