What Does GPT Stand For in Chat GPT?

GPT stands for “Generative Pre-trained Transformer”. GPT falls under the umbrella of large language models (LLM), which are language models based on a neural network with a large number of parameters.  

The GPT model has received widespread attention thanks to ChatGPT, a GPT-powered chatbot that produces human-like responses to prompts provided by users. 

ChatGPT has impressed users around the world with its impressive ability to perform a diverse set of tasks, for example explaining and generating code snippets, meaningfully summarizing long texts or even generating poems and jokes.

The background of the term “GPT”

The first GPT was released by US-based company OpenAI in 2018, but there’s also other projects that have since released similar models. Still, OpenAI’s GPT series of models remains the most notable. 

We should also note that OpenAI says “GPT” should be considered as a brand of the company and they have even attempted to trademark the term “GPT” when used int he field of artificial intelligence.

OpenAI has introduced several foundational models in their GPT series. They are labeled with a number, with each iteration having more advanced capabilities. The first model, GPT-1 was released in June 2018. OpenAI’s latest GPT model, GPT-4, was released in March of 2023. 

At the time of writing (June 2023), users can access the ChatGPT chat bot powered by GPT-3.5 for free. However, if you wish to use ChatGPT with the more advanced GPT-4 model, you have to pay a monthly subscription fee of $20.

GPT explained in simple terms

Now that we know what GPT stands for in Chat GPT, let’s explain how GPT works in simple terms. Here are the six most important concepts GPT is based on.

Pre-training

The model receives a very large dataset consisting of articles, books and websites sourced from the internet. In the pre-training phase, the model is focused on predicting the next word in the sentence based on the previous context. This phase allows the GPT model to get a general understanding of language, grammar and context.

Fine-tuning

Following the pre-training phase, the GPT model is trained with more specific datasets. This allows the model to be more effective in specific contexts, for example customer support conversations or even legal documents.

Input and tokenization

Users interact with a GPT model with prompts, which the model breaks down into smaller chunks called tokens. The size of tokens can range from a single character to a full word. The GPT model then processes the tokens with the goal of understanding prompt’s context and input as effectively as possible.

Context and attention mechanism

In order to understand how different tokens are related to each other, a GPT model uses an attention mechanism. Essentially, this involves assigning different weights to each token based on how relevant they are to the context. This allows a GPT model to generate responses that are coherent and contextually appropriate in relation to the prompt provided by the user.

Language generation

Once a GPT model has processed the input and established context, it generates text by predicting the most likely tokens to follow the given context. This can range from answering questions, completing sentences, or even creating entirely original text.

Iterative refinement

GPT generates text iteratively, predicting one token at a time based on the previous tokens. This process continues until a stopping condition is met, such as reaching a maximum length or generating a special token indicating the end of the response.

The downsides and limitations of ChatGPT

ChatGPT’s uncanny ability to process complex prompts and generate refined, human-like answers has sparked a surge of interest for AI (artificial intelligence) technologies. We’ve already seen this reflected in the stock market, and there has been an increase in funding for AI startups. Even in the crypto markets, AI-focused crypto projects have benefited significantly from the success of ChatGPT. 

However, it’s important to keep in mind that ChatGPT is far from infallible, as it will occasionally make bizarre mistakes or produce responses that are nonsensical. 

As a fun exercise, we asked ChatGPT to summarize some of downsides and limitations of GPT models. Here’s a part of the response:

“It’s important to note that while GPT can generate impressive text, it doesn’t possess true understanding or consciousness. It relies solely on patterns learned from training data and may occasionally produce incorrect or nonsensical responses. The quality of the generated text depends on the model’s training, the input provided, and the specific prompt or question given to it.”

Another downside of ChatGPT is that depending on which version of ChatGPT you use, the model can have limited knowledge of events that have happened after 2021. 

The bottom line – GPT is a very impressive technology, but it does have limitations

We’ve learned that the GPT in Chat GPT stands for “Generative Pre-trained Transformer”. This type of large language model is the underlying technology that gives ChatGPT its impressive capabilities to produce human-like and coherent responses to practically any prompt provided by the user.

If you’re trying to benefit from the growth of AI-powered technologies like ChatGPT, check out our article exploring the best ChatGPT stocks.

While the technology has plenty of uses, ChatGPT is not without its flaws. ChatGPT does not have true understanding of the material it’s working with, which is why it can sometimes produce incorrect or bizarre results. 

If you work with ChatGPT for an extended period of time, you’ll notice that it will occasionally falter even when it comes to operations that are straightforward for humans, such as simple arithmetic.