Large Language Models: How Tools Like ChatGPT Work
2 Training on Massive Text Corpora
LLMs are trained on a wide variety of internet text, books, articles, and other written sources. During training, the model learns:
1 Grammar and syntax.
2 Facts about the world (to a certain point in time).
3 Patterns of reasoning, dialogue, and style.
Training is done using a technique called unsupervised learning, where the model is given part of a sentence and asked to predict the next word. Over billions of examples, it gets very good at this task.
3 Transformer Architecture
At the core of ChatGPT is the transformer, a neural network architecture introduced in 2017. Its key components:
1 Self-attention mechanism: Allows the model to weigh the importance of different words in a sentence, even across long distances.
2 Layers of processing: Dozens or even hundreds of layers refine the representation of input text to capture meaning and context.
4 Fine-tuning and Alignment
After the initial training, models are fine-tuned on more specific data or made safer and more useful via Reinforcement Learning from Human Feedback (RLHF). This helps them:
1 Follow instructions better.
2 Avoid harmful or inappropriate responses.
3 Match user intent more accurately.
5 Generating Text
When you input a prompt, the model:
1 Converts the words into numbers (tokens).
2 Processes those tokens through the transformer layers.
3 Outputs a probability distribution for the next token (word piece).
4 Repeats this process to generate a full response.
You can control this with settings like temperature (randomness) or max tokens (length of response).
6 Continual Improvements
Though models like Chat GPT don’t “learn” after deployment in a traditional sense, they are updated periodically by developers:
1 Fixing weaknesses or biases.
2 Incorporating newer data.
3 Improving safety and efficiency.
