The architecture of ChatGPT is based on the Transformer architecture, which was introduced by Vaswani et al. in the paper "Attention Is All You Need".
The Transformer architecture consists of an encoder and a decoder, each of which is composed of multiple layers of self-attention and feedforward neural networks. The encoder processes the input sequence and generates a hidden representation for each token in the sequence, while the decoder uses these representations to generate the output sequence.
ChatGPT uses a modified version of the Transformer architecture that is optimized for language modeling. The model consists of multiple layers of self-attention and feedforward neural networks, with each layer performing a specific function in the language modeling task.
At each layer, the self-attention mechanism is used to compute a weighted sum of the input embeddings, which allows the model to focus on different parts of the input sequence at each step. The feedforward neural network is then used to apply non-linearity to the attention output, which generates a new representation for each token.
The architecture of ChatGPT is also characterized by its large number of parameters, which allows the model to capture complex relationships between words and phrases in natural language. The most recent version of ChatGPT, GPT-3, has 175 billion parameters, making it one of the largest language models in existence.
Overall, the architecture of ChatGPT has proven to be highly effective for generating high-quality natural language responses, and it represents a significant advancement in the field of natural language processing.
