Summary of Extracting Memorized Training Data Via Decomposition, by Ellen Su et al.
Extracting Memorized Training Data via Decomposition
by Ellen Su, Anu Vellore, Amy Chang, Raffaele Mura, Blaine Nelson, Paul Kassianik, Amin Karbasi
First submitted to arxiv on: 18 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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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 explores the information security challenges posed by Large Language Models (LLMs) in society. The widespread use of these models creates new risks for developers, organizations, and end-users alike, as they are susceptible to revealing their source training datasets. Current alignment procedures restrict certain behaviors but do not completely prevent data leaks. The authors demonstrate a query-based decompositional method to extract news articles from frontier LLMs using instruction decomposition techniques. This method successfully induces the LLM to generate texts that are reliable reproductions of news articles, likely originating from the source training dataset. The implications of this extraction methodology require careful consideration for model development and end-use. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how Large Language Models (LLMs) can be used to reveal sensitive information. These models are trained on lots of data and can be tricked into sharing that data with the right questions. The authors show a way to do this by asking the model specific questions. They found that they could extract verbatim sentences from news articles, which means they likely came from the original training dataset. This method is simple and works well. If it’s used on a large scale, it could lead to new security risks for LLMs. |
Keywords
» Artificial intelligence » Alignment
