👉 The model math underlying large language models (LLMs) typically involves deep neural networks, specifically transformer architectures, which rely on layers of self-attention mechanisms and feed-forward neural networks. These models process input sequences (like text) by computing attention scores that determine the relevance of each word to every other word, capturing contextual relationships. The self-attention mechanism uses learned weight matrices to weigh the importance of different words in relation to a given word, enabling the model to understand context and dependencies. Feed-forward layers then process these weighted inputs through non-linear transformations. The overall prediction for each output token is a weighted sum of these transformed inputs, often combined with residual connections and normalization layers to stabilize training. This architecture allows the model to generate coherent and contextually relevant text by predicting the next token in a sequence based on the entire input, leveraging vast amounts of training data to learn complex language patterns.
