Generative Pretrained Transformer

Generative Pretrained Transformers (GPT) are a class of language models that use transformer architecture to generate human-like text.

GPT models are widely used in natural language processing tasks such as text generation, translation, summarization, and question answering. They are pretrained on large corpora of text data and fine-tuned for specific tasks. The transformer architecture enables GPT models to handle long-range dependencies and generate coherent and contextually relevant text.

For example, a GPT model can generate a continuation of a given text prompt, producing human-like text that follows the context and style of the input. The model uses self-attention mechanisms to weigh the importance of different words in the input sequence, allowing it to generate high-quality text.

GPT models are based on the transformer architecture, which consists of an encoder and a decoder. However, GPT models typically use only the decoder part of the transformer for text generation. This is in contrast to the original transformer model, which uses both the encoder and decoder for tasks like translation.

In summary, GPT models are powerful tools for generating human-like text and performing various natural language processing tasks. They leverage the transformer architecture to handle complex language patterns and produce coherent and contextually relevant text. Their ability to generate high-quality text makes them valuable in applications such as chatbots, content creation, and automated writing.