Summary of Undesirable Memorization in Large Language Models: a Survey, by Ali Satvaty et al.
Undesirable Memorization in Large Language Models: A Survey
by Ali Satvaty, Suzan Verberne, Fatih Turkmen
First submitted to arxiv on: 3 Oct 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 The paper presents a comprehensive analysis of Large Language Models’ (LLMs) memorization phenomenon, exploring its risks and vulnerabilities. The authors examine the literature on LLM memorization across three dimensions: granularity, retrievability, and desirability. They discuss metrics and methods used to quantify memorization, identify causes and factors contributing to it, and explore strategies to mitigate undesirable aspects. The survey concludes by highlighting potential research topics for balancing privacy and performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are super smart computers that can understand and generate human-like text. But they also have some big problems. They tend to remember phrases from their training data, which makes them vulnerable to privacy and security attacks. This is a big deal because it could mean people’s personal information is at risk. The paper looks at all the research on this topic, trying to understand why it happens and how we can stop it. |
