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The Model Behind ChatGPT: How It Works
ChatGPT is based on the transformer architecture, which is a type of neural network that is specifically designed for natural language processing tasks such as language translation, question-answering, and text generation. In this article, we will explore the model behind ChatGPT in more detail.
Transformer architecture
The architecture consists of multiple layers, each of which contains a multi-headed self-attention mechanism and a feedforward neural network.
The self-attention mechanism allows the model to focus on different parts of the input sequence, which is important for language processing tasks where the meaning of a word can depend on the context in which it appears. The feedforward neural network provides non-linear transformations of the self-attention output.
Training
Training the ChatGPT model requires a large amount of data and computational resources. The model was trained using unsupervised learning, which means that it learned to predict the next word in a sequence of text without being given any specific examples of what the correct output should be.
During training, the model was exposed to a large number of sequences of text and learned to make predictions about the next word in the sequence based on the…
