Introduction to Large Language Models (LLMs)

 What are Large Language Models?

Large Language Models (LLMs) are a type of Artificial Intelligence (AI) model trained on massive amounts of text data to understand and generate human-like language.

They are designed to:

• Understand text
• Answer questions
• Translate languages
• Generate code, poems, or entire articles

Examples: OpenAI’s GPT (like ChatGPT), Google’s PaLM, Meta’s LLaMA, Anthropic’s Claude

How do LLMs Work?

LLMs are based on Transformer architectures, which use:

• Attention mechanisms to understand relationships between words
• Self-supervised learning to predict missing words
• Fine-tuning to specialize in specific tasks (e.g., legal, medical)

They are trained using billions (or trillions) of words from books, websites, and online conversations.

What Can LLMs Do?

Popular LLM Examples

Key Concepts to Know

• Tokens: Pieces of words used by LLMs for processing
• Parameters: Internal “settings” (can be billions!) learned by the model
• Prompting: Giving instructions to the model using natural language
• Fine-tuning: Teaching the model specific tasks after general training
• Hallucination: When an LLM confidently gives wrong or made-up info

Benefits of LLMs

• Can automate repetitive text-based tasks
• Save time in writing, coding, analysis
• Enable low-code/no-code solutions
• Assist in education, research, and customer support

Limitations of LLMs

• May generate inaccurate or biased responses
• Require large computing power
• Can hallucinate facts
• Need responsible and ethical use

Summary

• LLMs are powerful AI models trained on large text datasets.
• They understand and generate human language for many applications.
• Popular LLMs include GPT-4, PaLM, Claude, and LLaMA.
• Despite their power, LLMs must be used with caution and responsibility.