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Generative Pre-trained Transformer (GPT) refers to a type of artificial intelligence language model that uses a transformer architecture: 

• Generative: The model is capable of generating human-like text. It can produce coherent and contextually relevant responses based on the input it receives. 

• Pre-trained: The model is initially trained on a large dataset containing a wide variety of text from the internet. During this pre-training phase, the model learns to understand language patterns, grammar, facts, and context from diverse sources. 

• Transformer: The transformer architecture is a type of neural network architecture designed to handle sequential data efficiently.    Transformers use a mechanism called self-attention to process input data in parallel, making them well-suited for natural language processing tasks. [1]

In ChatGPT, GPT indicates that the model is based on the transformer architecture and has been pre-trained on a diverse range of internet text data to generate human-like responses in natural language. 

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[1] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. u. Kaiser, and I. Polosukhin, “Attention Is All You Need,” in Advances in Neural Information Processing Systems, I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, Eds., vol. 30. Curran Associates, Inc., 2017.