Google TechTalks

Google TechTalks

Differential PrivacyMachine LearningData PrivacyLarge Language Models (LLMs)Machine Learning SecurityData poisoningData SecurityPrompt Engineering
Private Adaptations of Large Language Models
Large Language Models (LLMs)Differential Privacy (DP)Machine Learning Adaptation

Private Adaptations of Large Language Models

Private adaptations of open-source Large Language Models (LLMs) offer superior privacy, performance, and cost-effectiveness compared to adapting closed-source LLMs, especially for sensitive data.

Explore Insights →
Cascading Adversarial Bias from Injection to Distillation in Language Models
Large Language Models (LLMs)Retrieval Augmented Generation (RAG)Adversarial Attacks

Cascading Adversarial Bias from Injection to Distillation in Language Models

RAG systems, designed to enhance LLM accuracy and personalization, are vulnerable to 'Phantom' trigger attacks where a single poisoned document can manipulate outputs to deny service, express bias, exfiltrate data, or generate harmful content.

Explore Insights →
Evaluating Data Misuse in LLMs: Introducing Adversarial Compression Rate as a Metric of Memorization
Large Language Models (LLMs)Data MemorizationCopyright Infringement

Evaluating Data Misuse in LLMs: Introducing Adversarial Compression Rate as a Metric of Memorization

This presentation introduces Adversarial Compression Rate (ACR) as a robust metric to quantify LLM memorization, addressing copyright concerns by focusing on the shortest prompt needed to elicit exact verbatim output.

Explore Insights →

Want more on prompt engineering?

Explore deep-dive summaries and actionable takeaways from the best minds across different podcasts discussing this topic.

View All Prompt Engineering Episodes→

Don't see the episode you're looking for?

We're constantly adding new episodes, but if you want to see a specific one from Google TechTalks summarized, let us know!

Submit an Episode