The core principle governing Large Language Models is that they function as sophisticated mathematical functions utilizing Transformer architectures with billions of trainable parameters to assign probability distributions over the next token sequence based on input context via attention mechanisms. Formally, these systems operate within the domain of probabilistic machine learning and deep neural networks where non-linear feedforward layers parallelize word encoding through self-attention operations to refine semantic representations emergently. This concept belongs to the discipline of natural language processing as a specific instantiation of autoregressive generation theories, distinct from rule-based or statistical n-gram approaches by its reliance on dense weight matrices tuned via backpropagation rather than explicit feature engineering.
The core principle governing Large Language Models is that they function as sophisticated mathematical functions utilizing Transformer architectures with billions of trainable parameters to assign pr…