Summary of Keep It Private: Unsupervised Privatization Of Online Text, by Calvin Bao and Marine Carpuat
Keep It Private: Unsupervised Privatization of Online Text
by Calvin Bao, Marine Carpuat
First submitted to arxiv on: 16 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper introduces an automatic text privatization framework that uses reinforcement learning to fine-tune a large language model, producing rewrites that balance soundness, sense, and privacy. The framework is evaluated on a large-scale test set of English Reddit posts by 68k authors, studying how performance changes among evaluative conditions including authorial profile length and authorship detection strategy. The method maintains high text quality according to both automated metrics and human evaluation, successfully evading several automated authorship attacks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a way to protect people’s privacy in online communications by rewriting text to hide the identity of the original author. It uses a special kind of machine learning called reinforcement learning to make sure the rewritten text makes sense and is natural-sounding while keeping the author anonymous. The researchers tested this method on a big dataset of short-medium length texts from Reddit, comparing how well it works under different conditions. They found that their method does a great job at maintaining high-quality text and staying hidden from automated authorship detection tools. |
Keywords
» Artificial intelligence » Large language model » Machine learning » Reinforcement learning
