Generative pre-trained transformer (GPT)

In the evolving world of artificial intelligence, the generative pre-trained transformer, commonly known as GPT, has emerged as a groundbreaking model in natural language processing.

What is GPT?

A generative pre-trained transformer (GPT) is a state-of-the-art large language model, or LLM, program that uses deep learning techniques to generate natural, human-like responses. 

OpenAI, an American AI research laboratory in California, created the first GPT. Since then, various other companies, including Microsoft's Bing and Google's Bard, have also developed generative pre-trained transformers.

How does GPT work?

A GPT is a natural language processing engine that generates coherent and contextually relevant text over extended passages. 

The big difference between a GPT and other chatbots is that a GPT doesn't rely on decision trees or pre-defined responses. 

GPT architecture is the first of its kind, using a pre-trained neural network to process data. This makes GPT the fastest and most intuitive NLP. It can provide eloquent, natural-sounding responses that are difficult to tell apart from humans.

The training process

There are two main stages in a GPT's training process: pre-training and fine-tuning. 

Pre-training

At its core, a GPT is pre-trained on text-based data and can only process language resulting from this closed training.

For example, you can pre-train a GPT exclusively on the anthology of William Shakespeare. Because its natural language processing would be modeled after Shakespeare's works, tone of writing, style, and language use, anything you'd generate on this particular GPT would come out structured theatrically using poetic Early Modern English.

Other GPTs—like ChatGPT, for instance—are pre-trained in a vast corpus of text in the public domain, including millions of pages from books, websites, and online articles. The pre-training phase allows the model to learn grammar, absorb facts, and develop reasoning abilities by predicting the next word in a sentence. 

Think of it as giving the model tons of homework to practice getting a general understanding of language. The larger the data set a GPT is pre-trained on, the more natural, accurate, and intuitive its responses will be

Fine-tuning

The second stage of training is fine-tuning. This is when programmers use narrower datasets to optimize the GPT for performing specific tasks, such as translation, summarization, computer coding, or answering questions. Fine-tuning sharpens the model's ability to perform specific tasks, making it more practical and effective. 

One component of the fine-tuning stage is setting a GPT's temperature parameter between 0 and 2. A temperature parameter determines how strictly a GPT adheres to its probability when predicting the next word in a sequence. For example, a temperature of 0 means the GPT will always select the most probable next word in its predictive model. On the other hand, a temperature value of 1.5 gives the GPT the option of selecting one of several high-probability words, resulting in more creativity and verbal diversity in its responses.

Understanding transformer architecture

The most powerful and groundbreaking element of a GPT is its underlying architecture: the transformer. You can think of the transformer as the engine inside the machine.

As a transformer weighs the importance of different words in a sentence, it uses attention mechanisms. Similar to the human concept of paying attention, these mechanisms allow a GPT to identify and focus on words and phrases that are more contextually relevant in a sentence, furthering its capacity to understand and reproduce natural language.

The sophistication of this mechanism is determined by how many parameters the GPT uses as part of its decision-making process. Each parameter is like a different step of a recipe. The more steps or parameters within its architecture, the more precise and intricate its language processing ability. 

Given the immense complexity of this process, many have even dubbed the transformer the black hole behind GPTs due to our imperfect understanding of how they work.

GPT evolutions

Generative pre-trained transformers have gone through four stages of evolution:

• GPT-1: Developed by OpenAI in 2018, the first iteration of a GPT laid the groundwork for modern NLPs.
   
• GPT-2: One year later, OpenAI released GPT-2, featuring 1.5 billion parameters. The code for this version of ChatGPT is open-source and available for free.
   
• GPT-3: In 2020, OpenAI released version 3 of its GPT, marking a breakthrough in language processing. 
   
• GPT-4: Released in March 2023, ChatGPT-4 is the model's newest and most powerful iteration, with a transformer architecture containing 1.7 trillion parameters. 

GPT applications

There are various ways to use GPTs, with more practical applications being developed every day. Here are some of the most common ways people use generative pre-trained transformers:

• Translations
• Simple coding
• Text generation
• Intuitive chatbots
• Document review
• Data-entry automation
• Content summarization