Emanuele Fabbiani

Inside the Mind of an LLM

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.

Inside the Mind of an LLM
#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.

Related jobs
Jobs that call for the skills explored in this talk.

Machine Learning Engineer

Picnic Technologies B.V.

Picnic Technologies B.V.
Amsterdam, Netherlands

Intermediate
Senior
Python
Structured Query Language (SQL)
+1

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