Generative Pre-trained Transformers (GPTs) are a type of language-processing AI model. They use a machine learning technique known as transformers, which allows them to generate human-like text by predicting the likelihood of a word given the previous words used in the text.

The original GPT (Generative Pre-trained Transformer) model was created by researchers at OpenAI in 2018. GPT pioneered the approach of pre-training a transformer neural network model on a large text corpus using a language modeling objective, before fine-tuning on downstream NLP tasks. This enabled breakthrough performance gains in NLP.

GPTs are pre-trained on a large corpus of text from the internet, which allows them to generate coherent and contextually relevant sentences by leveraging the patterns and structures they learned during this pre-training phase. After pre-training, they can be fine-tuned on specific tasks like translation, summarization, question-answering, and more.

GPTs are important for several reasons:

Versatility: Because they’re pre-trained on a large corpus of text, they have a broad understanding of language and can be fine-tuned for a wide variety of tasks.

Quality of Output: GPTs can generate impressively human-like text. The most recent version, GPT-3, can often generate text that is difficult to distinguish from text written by a human.

Efficiency: Because they use transformers, GPTs can process words in parallel, making them more efficient than previous types of language models that processed words sequentially.

Understanding Context: GPTs are good at understanding the context of a conversation or a piece of text, which makes them useful for tasks like chatbots or drafting emails.