Overview of Large Language Models (LLMs)

Introduction

GPT (Generative Pre-trained Transformer): A type of Large Language Model (LLM) that generates human-like text.
Topics Covered:
1. What is an LLM?
2. How LLMs work.
3. Business applications of LLMs.

Foundation Model: A broader category of pre-trained models on large amounts of unlabeled, self-supervised data.
LLMs: Specific type of foundation models applied to text (including code).
- Trained on large datasets (books, articles, conversations).
- Size: Tens of gigabytes, potentially petabytes of data.
- Example: A 1 GB text file can hold ~178 million words. A petabyte = ~1 million gigabytes.
- Parameter Count: LLMs have a high number of parameters (values a model can change as it learns).
  - Example: GPT-3 uses 175 billion ML parameters, trained on 45 terabytes of data.

Components:
1. Data: Massive amount of text data.
2. Architecture: Neural network, specifically a transformer in GPT's case.
  - Transformer Architecture: Handles sequences of data by understanding the context and relations between words.
3. Training: Process of the model learning to predict the next word in a sequence.
  - Training Process:
    - Begins with random guesses (e.g., “the sky is... bug”).
    - Iteratively adjusts parameters to reduce prediction errors.
    - Eventually generates coherent sentences (e.g., “the sky is blue”).
- Fine Tuning: Refining the model on a smaller, specific dataset to perform specialized tasks more accurately.

Customer Service: Intelligent chatbots to handle queries, allowing human agents to focus on complex issues.
Content Creation: Generate articles, emails, social media posts, video scripts.
Software Development: Assisting in code generation and review.
Future Applications: Potential for more innovative uses as LLMs continue to evolve.

Impact: LLMs are creating numerous new opportunities across various fields.
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