Large Language Models (LLMs) might seem complex, but the basic concepts behind them can be understood using simple arithmetic ideas you might have learned in middle school. Let’s break it down using an analogy of a curious detective!
The Giant Library (Database)
Imagine a detective (our LLM) who loves to read. She has a massive library containing lots of stories, conversations, and information. This library is like the data that LLMs are trained on. They read and learn from vast amounts of text, just like our detective reads her books.
Finding Clues (Training)
Our detective wants to understand the patterns in her library. For example, if she sees “Hello, how are you?” a lot, she notices that “Hello” is usually followed by “how are you?”. In the same way, LLMs learn patterns in their training data.
Think of it like finding the next number in a sequence. If you see 2, 4, 6, 8, you might guess the next number is 10 because you’ve spotted the pattern (+2 each time). LLMs do this with words and sentences.
Example:
- Sequence: 2, 4, 6, 8, 10
- Pattern: Each number is 2 more than the previous number.
- Prediction: The next number is 12.
