(22) Large Language Models explained briefly

Large Language Model (LLM)

A sophisticated mathematical function that predicts the next word for any given text, often with hundreds of billions of parameters.

Pre-training

The initial training phase where a model learns to autocomplete text from a vast amount of data.

Reinforcement Learning with Human Feedback

A training method where human feedback helps refine a model's predictions to make them more useful or preferred by users.

Backpropagation

An algorithm used to adjust the parameters of a model by comparing its predictions with the actual outcomes.

Transformer

A type of language model introduced in 2017 that processes text in parallel rather than sequentially.

Attention

An operation used in transformers that allows the model to weigh the importance of different words in relation to each other.

Parameters/Weights

Continuous values in a language model that determine the behavior and predictions of the model.

GPU (Graphics Processing Unit)

Specialized computer chips optimized for performing many calculations simultaneously, enabling faster model training.

Prediction Probability

A score assigned by the model indicating how likely each possible next word is, based on the preceding text.

Contextual Encoding

Process by which the meaning of a word is influenced by its surrounding words and derived semantic cues.