Lecture on Generative Pre-trained Transformers (GPT) and Large Language Models (LLMs)

Introduction

• GPT: Generative Pre-trained Transformer
• LLM: Large Language Model that generates human-like text
• Video will cover:
  1. What is an LLM?
  2. How do LLMs work?
  3. Business applications of LLMs

What is a Large Language Model (LLM)?

• LLM is a type of foundation model
• Pre-trained on large amounts of unlabeled, self-supervised data
• Learns from patterns to produce generalizable and adaptable output
• Applied specifically to text, including code
• Trained on large datasets such as books, articles, and conversations
• Can be tens of gigabytes in size and trained on potentially petabytes of data
• Example: 1 GB text file = ~178 million words; 1 petabyte = 1 million GB
• LLMs have large parameter counts (e.g., GPT-3 has 175 billion parameters)

How Do LLMs Work?

• Components of LLMs: Data, Architecture, Training
• Data: Enormous amounts of text data
• Architecture: Neural network, specifically the Transformer architecture
  • Handles sequences of data (sentences, lines of code)
  • Understands context by considering each word in relation to others
  • Builds comprehensive understanding of sentence structure and word meanings
• Training:
  • Model learns to predict the next word in a sentence
  • Starts with random guesses and adjusts parameters to reduce prediction errors
  • Gradually improves word predictions to generate coherent sentences
  • Fine-tuning: Refines understanding on a smaller, specific dataset for specific tasks

Business Applications of LLMs

• Customer Service: Intelligent chatbots handling customer queries, freeing up human agents
• Content Creation: Generate articles, emails, social media posts, video scripts
• Software Development: Generate and review code
• Potential for more innovative applications as LLMs evolve

Conclusion

• LLMs are versatile tools with a wide range of applications
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