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- Two-Step Method Overview
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Classroom Contents
Extracting Training Data from Large Language Models - Paper Explained
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- 1 - Intro & Overview
- 2 - Personal Data Example
- 3 - Eidetic Memorization & Language Models
- 4 - Adversary's Objective & Outlier Data
- 5 - Ethical Hedging
- 6 - Two-Step Method Overview
- 7 - Perplexity Baseline
- 8 - Improvement via Perplexity Ratios
- 9 - Weights for Patterns & Weights for Memorization
- 10 - Analysis of Main Results
- 11 - Mitigation Strategies
- 12 - Conclusion & Comments
