Summary
Language models such as GPT-4 are built on a foundation of complex neural network architectures and processes, each serving critical roles in understanding and generating text. These models start with extensive training data encompassing a diverse array of topics and writing styles, which is then processed through tokenization to convert text into a numerical format that neural networks can work with. GPT-4, specifically, employs the Transformer architecture, which eliminates the need for sequential data processing inherent to RNNs and leverages self-attention mechanisms to weigh the importance of different parts of the input data. Embeddings play a crucial role in this architecture by converting words or tokens into vectors that capture semantic meaning and incorporate the order of words through positional embeddings.
User interaction significantly influences the performance and output quality of models such as GPT-4. Through prompts, feedback, and corrections, users shape...
