Generative Pre-Trained Transformers (GPT)

Generative Pre-Trained Transformers (GPT) are a type of large language models (LLM) and a prominent framework for generative artificial intelligence. The concept and the first such model were introduced in 2018 by the American artificial intelligence association OpenAI. GPT models are artificial neural networks that are based on a transformer architecture, pre-trained on large corpus of unsupervised text, and are able to generate new, human content.

OpenAI has released highly influential GPT foundation models, which have been sequentially numbered to form the “GPT-n” series. Each was significantly more capable than the last, thanks to increased size (number of parameters to train) and training. The latest of these, GPT-4, was released in March 2023. Such models became the basis for more specific GPT systems, including models refined to execute instructions, which in turn power the ChatGPT chatbot service.

History and evolution of GPT models

Generative pre-training (GP) has been a long-established concept in machine learning applications, but the transformer architecture was not available until 2017, when it was invented by Google. This development led to the emergence of large language models such as BERT in 2018 and XLNet in 2019, which were pre-trained with transformers (PT) but were not designed to generate (they were “just encoders”).

Before transformer-based architectures, the best-performing NLP (natural language processing) models often used supervised learning from large amounts of manually labeled data. Their reliance on supervised learning limited their use on datasets that were not well labeled, and also made training very large language models extremely expensive and time-consuming.

The semi-supervised approach that OpenAI used to create a large-scale generative system – and was the first to do so with a transformer model – involved two stages: an unsupervised generative “pre-training” stage to set initial parameters using a language modeling target, and a supervised discriminative “refining” step to adapt these parameters to a specific task.

GPT foundation models

Foundation models are AI models trained on a wide range of data to the extent that they can be adapted to a wide range of tasks in the following streams.

The most important GPT foundation models come from OpenAI’s GPT-n series. The latest of these is GPT-4, for which OpenAI declined to publish size or training details (citing “the competitive landscape and security implications of large models”

Key elements of natural language processing

ChatGPT is an artificial intelligence (AI) chatbot that uses natural language processing (NLP) to create human-like dialogues. The language model can answer questions and generate various types of written content, such as articles, social media posts, essays, code, and emails.

Creators of ChatGPT

ChatGPT was created by OpenAI – an AI research company – in November 2022. OpenAI was founded by a group of entrepreneurs and researchers, including Elon Musk and Sam Altman, in 2015. Microsoft is one of the company’s most important investors. OpenAI is also the creator of Dall-E, an AI art generator.

How does ChatGPT work?

ChatGPT works thanks to the Generative Pre-trained Transformer, which uses specialized algorithms to find patterns in data sequences. ChatGPT uses the GPT-3 language model, a machine learning model with a neural network, and a third-generation Generative Pre-trained Transformer.

The types of questions users can ask ChatGPT

Users can ask ChatGPT a variety of questions, from simple to more complex. You can ask, for example, “What is the meaning of life?” or “What year did New York become a state?” ChatGPT is proficient in STEM fields and can debug or write code.

Various uses of ChatGPT

ChatGPT is versatile and can be used for more than just human conversations. People have used ChatGPT to:

• Coding computer programs.
• Creating music.
• Writing emails.
• Summaries of articles, podcasts or presentations.
• Creating posts for social media.
• Creating titles for articles.
• Solving math problems.
• Keyword research for search engine optimization.
• Creating articles, blog posts and quizzes for websites.
• Rephrasing existing content for another medium, e.g. a transcript of a presentation for a blog post.

ChatGPT limitations and its accuracy

ChatGPT has some limitations:

• Does not fully understand the complexity of human language. ChatGPT is trained to generate words based on the entered text. Because of this, the answers may seem superficial and lack real insight.
• Lack of knowledge about data and events after 2021. Training data ends with 2021 content. ChatGPT may provide incorrect information based on the data it draws from. If ChatGPT does not fully understand the query, it may also provide an incorrect response.

ChatGPT training and development

ChatGPT is part of the family of Generative Pre-Trained Transformer (GPT) language models. It has been polished on OpenAI’s improved GPT-3, known as “GPT-3.5”. The grinding process uses both supervised learning and reinforcement learning.

Conclusions

Despite some limitations, ChatGPT and the technology behind the GPT acronym are an exceptional example of progress in the field of artificial intelligence. Understanding how it works and what capabilities it has allows you to better use its potential and understand how AI affects our world.