Large Language Models-LLMs

LLMs are based on deep learning models trained on massive datasets, from books and news articles to online forums and academic papers. They use transformer architectures and contain billions (or even trillions) of parameters, enabling them to recognize language patterns, connect ideas across long passages, and generate new content in real time.

Key Processes

Tokenization: Text is broken into tokens, which are the basic units the model uses to process and predict language.
Contextual prediction: The model analyzes the surrounding text to choose the most probable next token.
Transformer architecture: Enables the model to consider all tokens in relation to each other, rather than in isolation.
Pretraining: LLMs are exposed to enormous amounts of text data to learn grammar, meaning, reasoning, and even cultural nuance.
Fine-tuning: After initial training, many LLMs are adjusted using specific data or human feedback to improve their alignment with real-world tasks.
Reinforcement learning from human feedback (RLHF): Helps the model provide safer, more helpful, and more ethical responses.