Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

YouTube

Extracting Training Data from Large Language Models - Paper Explained

Yannic Kilcher via YouTube

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
Explore a comprehensive video analysis of a research paper that uncovers a method for extracting verbatim training data from large language models. Delve into the security and privacy implications for models like GPT-3, as the presenter breaks down the paper's findings, methodology, and results. Learn about eidetic memorization in language models, the adversary's objectives, and the two-step extraction method. Examine the analysis of main results, including the vulnerability of larger models, and consider proposed mitigation strategies. Gain insights into the ethical concerns surrounding the publication of large language models trained on private datasets and the potential risks of exposing personally identifiable information.

Syllabus

- Intro & Overview
- Personal Data Example
- Eidetic Memorization & Language Models
- Adversary's Objective & Outlier Data
- Ethical Hedging
- Two-Step Method Overview
- Perplexity Baseline
- Improvement via Perplexity Ratios
- Weights for Patterns & Weights for Memorization
- Analysis of Main Results
- Mitigation Strategies
- Conclusion & Comments

Taught by

Yannic Kilcher

Reviews

Start your review of Extracting Training Data from Large Language Models - Paper Explained

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.