ChatGPT uses a transformer-based architecture for natural language processing. Specifically, it uses a variant of the transformer architecture known as the "decoder-only transformer", which was introduced in the original GPT model.
The transformer architecture is a type of deep neural network that has been shown to be particularly effective for natural language processing tasks. It is based on a set of encoder and decoder layers, which are stacked together to form the overall model architecture.
In the case of ChatGPT, the architecture consists of a stack of multiple decoder-only transformer layers, with each layer consisting of a self-attention mechanism and a feedforward neural network. The self-attention mechanism allows the model to weigh the importance of different words in a sequence when processing each word, while the feedforward neural network allows the model to learn complex nonlinear relationships between words and phrases.
Additionally, the model uses a positional encoding mechanism that allows it to take into account the order of words in a sentence. This is important for natural language processing tasks, as the order of words can significantly impact the meaning of a sentence.
Overall, the architecture of ChatGPT is a highly effective and sophisticated deep learning model that is capable of generating high-quality, human-like text across a wide range of natural language processing tasks.