Inside GPT – Large Language Models Demystified • Alan Smith • GOTO 2024

GPT models fundamentally operate by predicting the next token in a sequence through a process of tokenization and probability distribution calculation

The model converts text into tokens (not words) which are represented as vectors in 768-dimensional space through embedding, allowing mathematical operations to analyze relationships between tokens

Temperature (0-1) and Top P are key parameters that control output randomness - temperature affects probability distribution while Top P sets hard cutoffs for token selection

Models use attention mechanisms with query, key and value vectors to understand relationships between words/tokens. GPT-2 has 12 attention heads in 12 layers

Position of tokens matters significantly - models use positional encodings to understand token order and context through sine/cosine calculations

English is most efficient for these models due to training data availability - other languages require more tokens to convey the same meaning, increasing costs

The models don’t truly “understand” text - they perform statistical analysis of token relationships to predict likely next tokens in sequences

Scaling up models (GPT-3, GPT-4) primarily involves adding more layers and parameters rather than fundamental architectural changes

Models operate entirely through floating point mathematics and matrix operations - there is no actual language understanding, just statistical pattern matching

Testing and analyzing model outputs requires considering multiple runs with varied parameters since outputs have inherent randomness/stochasticity