Large language Models

GPT 4o describes how LLMs work (24.5.24)

Large language models work by using deep learning techniques to process and generate human-like text. Here's a simple explanation of how they work:

First, the model is trained on a huge amount of text data from various sources like books, websites, and articles. This training helps it learn the patterns and structures of the language.

During training, the model uses a type of artificial neural network called a transformer. Transformers are particularly good at understanding context and relationships between words in a sentence. They achieve this through a mechanism called "attention," which allows the model to focus on different parts of the input text when making predictions.

When you input a sentence or a question, the model analyzes the text, processes it through its neural network layers, and predicts the most likely next word or response based on what it learned during training.

The key components are:

1. Tokenization: Breaking down text into smaller units called tokens (words or subwords).

2. Embedding: Converting tokens into numerical vectors that represent their meanings.

3. Attention Mechanisms: Focusing on relevant parts of the input to understand context.

4. Decoding: Generating a coherent and contextually appropriate response.

Because of the vast amount of data and the complex architecture, the model can generate responses that are surprisingly accurate and human-like.