Introduction to Large Language Models

30 Oct 2024

🚧 Work in progress…

This chapter provides an overview of Large Language Models (LLMs), covering their architecture, training methodologies, and optimization techniques.

What you’ll learn in this chapter:

Large Language Models have revolutionized natural language processing and artificial intelligence. This chapter explores the fundamental concepts and advanced techniques that power modern LLMs.

Core Concepts

• Tokenization: How text is converted into numerical representations that models can process
• Attention Mechanisms: The core innovation that enables models to focus on relevant parts of the input

Training and Optimization

• Fine-tuning: Adapting pre-trained models to specific tasks and domains
• Alignment Methods: Techniques for aligning model behavior with human preferences
  • PPO (Proximal Policy Optimization): Policy gradient methods for RLHF
  • DPO (Direct Preference Optimization): Simplified preference learning without reward models
  • GRPO (Group Relative Policy Optimization): Advanced policy optimization techniques
  • RLAIF (Reinforcement Learning from AI Feedback): Scaling alignment with synthetic feedback
  • RLVF (Reinforcement Learning from Verifiable Feedback): Alignment with verifiable correctness

By the end of this chapter, you’ll have a comprehensive understanding of how LLMs are built, trained, and optimized for various applications.