Lecture Notes: Introduction to Large Language Models (LLMs)

Overview

• Presenter’s experience with GPT and LLMs
• Three main questions addressed:
  1. What is an LLM?
  2. How do LLMs work?
  3. Business applications of LLMs

1. What is a Large Language Model (LLM)?

• LLM: A type of foundation model specialized in text and text-like data (e.g., code)
• Foundation Model: Pre-trained on large amounts of unlabeled, self-supervised data
  • Learns from patterns in the data
  • Produces generalizable and adaptable output
• Training data: Books, articles, conversations, etc.
  • Models can be tens of gigabytes to petabytes in size
  • Example: 1 gigabyte ≈ 178 million words
  • 1 petabyte = 1 million gigabytes
• Parameters: Values the model can change independently to learn
  • More parameters = more complex model
  • Example: GPT-3 uses 175 billion parameters, pre-trained on 45 terabytes of data

2. How do LLMs Work?

• Key Components:
  • Data: Enormous amounts of text data
  • Architecture: Neural network (specifically, a transformer for GPT)
    • Transformer architecture handles sequences of data
    • Understands context of each word in relation to others
    • Builds comprehensive understanding of sentence structure and meaning
  • Training:
    • Predicts the next word in a sentence
    • Starts with random guesses, updates internal parameters iteratively
    • Gradual improvement to generate coherent sentences
    • Fine-tuning: Refining the model for specific tasks using smaller datasets

3. Business Applications of LLMs

• Customer Service: Intelligent chatbots to handle customer queries
• Content Creation: Generating articles, emails, social media posts, and video scripts
• Software Development: Generating and reviewing code

Future Prospects

• Continued evolution of LLMs expected
• More innovative applications anticipated

Conclusion

• Encouragement to ask questions and subscribe for more content

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