👉 The model employs a combination of linear algebra and calculus to process and generate text. Linear algebra is used for embedding words into high-dimensional vector spaces, where semantic relationships between words can be captured and manipulated mathematically. This allows the model to understand context and generate coherent text by transforming input into these vectors and performing operations like matrix multiplication. Calculus, particularly gradient descent, is utilized during training to minimize the loss function, which measures the difference between the model's predictions and actual outputs. This optimization process adjusts the model's parameters iteratively, refining its ability to predict and generate text that closely matches human language patterns. The interplay of these mathematical disciplines enables the model to learn from vast datasets and produce contextually relevant and grammatically correct responses.
