RCADT - GPT: Generative Pre-trained Transformer

A Generative Pre-trained Transformer (GPT) is a type of large language model (LLM) that uses a specific neural network architecture to understand and generate human-like text. The theory behind GPT is built on three core pillars:

1. Generative (The Objective)
The "Generative" aspect refers to the model's primary goal: creating new content rather than just classifying existing data.
Autoregressive Prediction: GPT operates by predicting the next most probable word (or "token") in a sequence based on all preceding words.
Sequential Generation: Once a token is predicted, it is added back to the input, and the process repeats until a complete response is formed or an "end" token is reached.

2. Pre-trained (The Learning Process)
Before a GPT model can follow specific instructions, it undergoes a massive phase of unsupervised learning.
Massive Datasets: The model is fed billions of pages from the internet, books, and articles.
Self-Supervised Learning: It learns the statistical structure of language (grammar, facts, reasoning) by trying to predict the next word in these texts without human-labeled help.
Fine-tuning: After pre-training, models are often refined using Reinforcement Learning from Human Feedback (RLHF) to align their responses with human values and safety.

3. Transformer (The Architecture)
The "Transformer" is the underlying deep learning engine, introduced in the 2017 Google paper "Attention Is All You Need".
Self-Attention Mechanism: This is the "brain" of the model. It allows the GPT to "attend" to different parts of a sentence simultaneously to understand context.
Decoder-Only Design: Unlike some transformers that use both encoders and decoders, GPT specifically uses a stack of decoder blocks optimized for generating text one step at a time.
Parallel Processing: This architecture allows the model to process large amounts of data in parallel rather than one word at a time, making training much faster.

https://youtu.be/EzOeZoG-Rq4

Видео RCADT - GPT: Generative Pre-trained Transformer канала HYPOTHALAMUS Ai