![[1hr Talk] Intro to Large Language Models](https://i.ytimg.com/vi/zjkBMFhNj_g/hqdefault.jpg)
What You'll Learn
- Understand the fundamental workings of Large Language Models, including their training and inference processes.
- Identify the limitations and potential security vulnerabilities of current LLMs, such as jailbreaking and data poisoning.
- Explore the future directions of LLM development, including self-improvement, customization, and the integration of tool use and multimodality.
Video Breakdown
This video provides an introduction to Large Language Models (LLMs), covering their functionality, training processes, and the current landscape of open and closed-source options. It explores the limitations of current LLMs, comparing their 'system one' thinking to human 'system two' thinking, and discusses potential improvements through self-improvement, customization, and tool use. The video also addresses the emerging security challenges, such as jailbreak attacks and data poisoning, highlighting the ongoing efforts to improve LM security and robustness.
Key Topics
LLM Training
Open vs Closed Source
System One Thinking
Security Vulnerabilities
Prompt Injection
Data Poisoning
Video Index
Introduction to Large Language Models
This module introduces the concept of large language models, using Llama 2 70B as an example, and ex...
This module introduces the concept of large language models, using Llama 2 70B as an example, and explains their basic functionality and training process.
What are LLMs and How Do They Work?
0:00 - 12:03
Explains the fundamental components of LLMs, including parameters and code, and the process of next word prediction.
Llms
Model Architecture
Next Word Prediction
Training Data
Training and Evaluation of LLMs
This module covers the training methodologies used for LLMs, including pre-training, fine-tuning, an...
This module covers the training methodologies used for LLMs, including pre-training, fine-tuning, and reinforcement learning from human feedback (RLHF), along with evaluation metrics and leaderboards.
The Two-Stage Training Process
12:00 - 24:03
Details the pre-training and fine-tuning stages, including the optional use of comparison labels and human-machine collaboration.
Pre-Training
Fine-Tuning
Human Feedback
Label Creation
The Current Landscape and Future Directions
This module explores the current state of language models, comparing open and closed-source options,...
This module explores the current state of language models, comparing open and closed-source options, and discusses scaling laws, tool use, multimodality, and future directions like system one versus system two thinking.
Open vs Closed Source and Scaling Laws
24:02 - 36:06
Compares different language model options and discusses the impact of scaling laws on model capabilities.
Open Source Llms
Closed Source Llms
Scaling Laws
Chatgpt Example
Limitations and Improvements of LLMs
This module discusses the limitations of current LLMs, comparing 'system one' and 'system two' think...
This module discusses the limitations of current LLMs, comparing 'system one' and 'system two' thinking, and explores potential avenues for improvement, including self-improvement and customization.
Addressing the Limitations of System One Thinking
36:04 - 48:08
Explores the cognitive limitations of current LLMs and potential strategies for improvement, such as reward functions and task-specific customization.
System One Thinking
System Two Thinking
Reward Functions
Customization
Security Vulnerabilities and Attack Methods
This module covers various attack methods on large language models, including jailbreaking, prompt i...
This module covers various attack methods on large language models, including jailbreaking, prompt injection, and data poisoning, and discusses the ongoing efforts to improve LM security.
Understanding LLM Attacks
48:06 - 59:49
Details different attack vectors, such as jailbreaking via encoding, universal suffixes, and adversarial examples, and the challenges of defending against them.
Jailbreaking Llms
Prompt Injection Attacks
Data Poisoning
Adversarial Examples
Defense Mechanisms
Questions This Video Answers
What are Large Language Models (LLMs) and how do they work?
LLMs are AI models trained on vast amounts of text data to predict the next word in a sequence. They consist of parameters and code, requiring significant computational resources for training.
What is the difference between pre-training and fine-tuning?
Pre-training involves training the model on a large, general dataset, while fine-tuning adapts the model to a specific task or domain using a smaller, more focused dataset.
What are some of the security risks associated with LLMs?
LLMs are vulnerable to attacks like jailbreaking, prompt injection, and data poisoning, which can compromise their intended behavior and security.
How can LLMs be improved in the future?
Future improvements include self-improvement through reward functions, customization for specific tasks, and the integration of tool use and multimodality to enhance their capabilities.
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