An LLM leaking private data isn't a bug, it's a core feature. Learn why deep learning models are fundamentally designed to memorize unique information.
#1about 7 minutes
Understanding the risks of large language models
LLMs are often used without understanding their inner workings, leading to factual errors and the generation of insecure code.
#2about 8 minutes
How large language models are trained
A four-phase process explains how models learn language through pre-training, are taught tasks, aligned with human preferences, and refined using reinforcement learning.
#3about 5 minutes
Why Llama 2 models think in English
Research on Llama 2 models reveals they use English as an internal representation for all tasks due to its prevalence in the training data.
#4about 4 minutes
Controlling LLM behavior with monosemantic features
By identifying and amplifying single-meaning concepts, or monosemantic features, it is possible to deterministically control a model's output on specific topics.
#5about 2 minutes
Why LLMs memorize and leak private data
Deep learning models inherently memorize unique outlier data from their training set, which explains why LLMs can leak personal information and pose a privacy risk.
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