Lecture Notes on Large Language Models (LLMs)

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

• Definition: An LLM is an instance of a foundation model, specifically applied to text and text-like data (e.g., code).
• Foundation Models: Pre-trained on vast amounts of unlabeled and self-supervised data.
• Data Size: LLMs can be tens of gigabytes and trained on potentially petabytes of data.
  • Example: 1 gigabyte of text = ~178 million words.
  • 1 petabyte = ~1 million gigabytes.
• Parameter Count: LLMs have a high number of parameters, increasing their complexity.
  • Example: GPT-3 uses 175 billion ML parameters and is trained on 45 terabytes of data.

2. How Do Large Language Models Work?

• Components of LLM:
  1. Data: Enormous datasets of text.
  2. Architecture: Neural network architecture, specifically transformers.
  3. Training: Learning process to improve predictions.
• Transformers:
  • Handle sequences of data (sentences, lines of code).
  • Understand context by relating each word to all others in a sentence.
• Training Process:
  • Model predicts the next word in a sentence.
  • Example: Starts with a random guess, adjusts internal parameters based on correct outcomes.
  • Gradually improves until it generates coherent sentences.
• Fine-Tuning:
  • The process of refining an LLM on specific datasets to enhance performance for particular tasks.

3. Business Applications of LLMs

• Customer Service:
  • Intelligent chatbots can handle customer queries, allowing human agents to focus on complex issues.
• Content Creation:
  • Generate articles, emails, social media posts, and video scripts.
• Software Development:
  • Assist in generating and reviewing code.
• Future Potential: As LLMs evolve, more innovative applications are likely to emerge.

Conclusion

• Enamored with the potential of LLMs.
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