How Large Language Models Work

From Zero to ChatGPT

What Is a Large Language Model?

A large language model (LLM) is a computer program that can read, understand, and write text like a person. It learns by looking at huge amounts of text from books, websites, and other sources.

Example:
If you ask, “What’s the capital of France?” the model can answer, “Paris.”

The Basics: How It Starts

• Data Collection: The model is fed billions of words from the internet, books, and articles.

  • Example: It reads sentences like “The cat sat on the mat.”

• Tokenization: The text is broken down into small pieces called tokens.

  • Example: “unhappiness” becomes “un,” “happi,” and “ness.”

• Learning Patterns: The model looks for patterns in how words and sentences are used.

  • Example: It learns that “good morning” is a common greeting.

The Engine: Transformer Architecture

• Transformer Model: Most LLMs use a design called a transformer.

  • Example: The model can tell that “bank” means a place for money in “I went to the bank,” but means a river’s edge in “I sat by the bank.”

• Attention Mechanism: The model pays attention to important words in a sentence.

  • Example: In “The dog that chased the cat was fast,” it knows “dog” is the one that was fast.

• Layers: The model has many layers of artificial neurons.

  • Example: Each layer helps the model get better at understanding, like stacking filters to see a clearer picture.

Training: From Zero to Smart

• Pre-Training: The model tries to guess the next word in a sentence.

  • Example: Given “The sky is ___,” it learns to fill in “blue.”

• Fine-Tuning: The model is trained on more specific tasks.

  • Example: It learns to answer questions or summarize news articles.

• Human Feedback: People review the model’s answers and give feedback.

  • Example: If the model says “Paris is in Germany,” a person corrects it.

How Does It Work in Practice?

• Input: You type a question or prompt.

  • Example: “Write a poem about rain.”

• Processing: The model turns your words into tokens and runs them through its layers.

• Prediction: It predicts the next word, then the next, until it forms a complete answer.

  • Example: “Rain falls softly on the ground, making a gentle, soothing sound.”

• Output: You get a response that sounds natural and makes sense.

From LLMs to ChatGPT

• ChatGPT: This is a chatbot built on top of a large language model called GPT.

  • Example: You ask, “Can you help me with my homework?” and it gives you tips or answers.

• Improvements: Newer versions can handle more complex tasks and longer conversations.

  • Example: They can write stories, solve math problems, or explain science topics.

Key Points

• LLMs learn by reading lots of text.

  • Example: They read millions of sentences like “Birds can fly.”

• They use transformers to understand context.

  • Example: They know “bark” means a dog’s sound or tree covering, depending on the sentence.

• Training happens in two main steps: pre-training and fine-tuning.

  • Example: First, they learn general language; then, they learn to answer questions.

• ChatGPT is a popular example that uses these ideas to chat with people.

Table: Steps from Zero to ChatGPT

Large language models make tools like ChatGPT possible. They help people get answers, write text, and much more.