What is GPT-4

GPT-4: What You Need to Know And What's Different From GPT-3

As ChatGPT is still all the rage and has been downloaded one million times in the first couple of days after its release, anticipation runs high for GPT-4, the long awaited successor of GPT-3. GPT-4 is going to be a further development of GPT-3 and the still new ChatGPT, bringing with it a range of new capabilities and enhancements that make it more powerful than its GPT-predecessors.

But what is the GPT-4 hype all about and what can we really expect from it? Read all about what we know about GPT-4 (so far) in the following.

The contents of this article are:

  1. What is GPT And What Can It Be Used For?
  2. GPT-3 vs. data-to-text – What’s the difference?
  3. What Is GPT-4 And When Will It Be Released?
  4. What Will Be The Difference Between GPT-4 And Its Predecessor GPT-3?
  5. What Will GPT-4 Look Like?
  6. Scope of Application for GPT-4
  7. What Is ChatGPT?
    7.1 What Is The Use of ChatGPT?
    7.2 The Limitations Of ChatGPT
  8. What is the future of GPT?

1. What is GPT And What Can It Be Used For?

GPT stands for Generative Pre-trained Transformer and is a model that uses deep learning to produce human-like language. The NLP (natural language processing) architecture was developed by OpenAI, a research lab founded by Elon Musk and Sam Altman in 2015.

GPT uses a large corpus of data to generate human-like language representations. It is a language model that learns from existing text and can provide different ways to end a sentence. It has been trained with hundreds of billions of words, representing a significant portion of the internet - including the entire corpus of the English Wikipedia, countless books, and a dizzying number of webpages.

GPT can be used for various tasks and many practical applications such as summarization, question answering, translation, market analysis, and much more.

The latest model of the GPT series is GPT-3. But there is another big language model that generates texts automatically: data-to-text. But how do these two models differ and in which cases are they applicable?

2. GPT-3 vs. Data-to-Text – What’s the Difference?

Both GPT-3 and data-to-text are NLG technologies. NLG means "Natural Language Generation" and refers to the automated generation of natural language text. At first glance, they may seem quite similar, but they work very differently. Some of these differences are listed here:

DifferentiatorsData-to-TextGPT-3
Text generation bybased on structured data (attributes available in, e.g., tables, like product features from a PIM system or match results of a soccer game)learns from existing text (trained with hundreds of billions of words from, e.g. Wikipedia, books, and numerous  web pages)
Control over contentuser has control over text result at all timesuser has no control over generated content
Text qualitytext consistency, meaningfulness, and qualitytexts need to be fact checked; might give wrong or improper information 
Scalabilitytexts are customizable and scalablegenerate individual texts
Languagesmultilingual content creation in up to 110 languagesmonolingual content creation only
UsageFor creating large amounts of text based on structured data sets with variable detailsTo create a basic text; can simplify writing process

Because of these differing qualities, GPT-3 and data-to-text are suitable for different applications.

In short, data-to-text is used in e-commerce, the financial and pharmaceutical sector, in media and publishing.
GPT-3 can be helpful for brainstorming and in finding inspiration, for example, if the user is suffering from writer’s block. To use GPT-3 in chatbots to answer recurring customer queries is quite useful as well, as having humans generate the text output is inefficient and impractical.

Learn more about GPT-3 vs. Data-to-text: What’s the Right Content Generation Technology for Your Business?

3. What Is GPT-4 And When Will It Be Released?

GPT-4 is going to be the latest version of GPT developed by OpenAI. As with any of the GPTs, GPT-4 is being trained on a massive amount of data and will be able to generate human-like text for multiple tasks. It is supposed to be able to produce high-quality content, including blog articles, reports, and news articles.

Officially, GPT-4 is still in development. Although no release date has been announced, speculations run high that it will be released in the upcoming months and that it will be available in the course of 2023.

4. What Will Be The Difference Between GPT-4 And Its Predecessor GPT-3?

As GPT-3 has already left quite an impression in its capabilities after its release in 2020, expectations for GPT-4 run high. OpenAI has made a big secret out of it, but some details have seeped through. In brief, the supposed improvements of GPT-4 in comparison to GPT-3 and ChatGPT are processing more complex tasks with improved accuracy, scalability, and alignment. This will allow for a wider range of applications.

The biggest difference between GPT-3 and GPT-4 is shown in the number of parameters it has been trained with. GPT-3 has been trained with 175 billion parameters, making it the largest language model ever created up to date. In comparison, GPT-4 is likely to be trained with 100 trillion parameters. Some argue that this will bring the language model closer to the workings of the human brain in regards to language and logic.

Size doesn’t matter – GPT-4 won’t be bigger than GPT-3

However, in its goal to mimic the human language, GPT-4 will have a huge advantage over GPT-3 for its training on so many parameters and huge data input. It will be a big step forward in the GPT models capability of analyzing text input and processing inquiries. Because of its comprehensive training, GPT-4 will also give a lot more choices of sentence continuations as well as voices and styles.

In conclusion, this means that GPT-4 will think even more human-like than any other GPT model so far. Quite astounding, as in a Q&A at the AC10 online meetup in 2021, OpenAI’s CEO Sam Altman said that GPT-4 is not going to be bigger than GPT-3. So it won’t exceed GPT-3 in size. Altman explained that OpenAI’s goal is not to build a massive language learning model, but to focus on improving their GPT models’ performances.

As we explained above, data-to-text uses structured data provided by the user as the basis for text generation. This means more control over text design and text quality. So if you are considering using text automation, you should take a close look at which of the two technologies is right for your requirements. In this video, AX Semantics CEO Saim Alkan explains the difference between content results from data-to-text tools like AX Semantics and those from GPT-3 tools:

5. What Will GPT-4 Look Like?

GPT-4 can be trained on any language dataset. Next to data, OpenAI will also focus on algorithms, alignment and parameterization. Therefore, GPT-4 could support a variety of hyperparameters. As a GPT model, it will have an improved transformer architecture for a better understanding of relationships between words in text.

What we can expect of GPT-4:

1. Alignment
Alignment is posing a challenge for OpenAI. As they aim for their language models to be able to interact with and understand the intentions of users, they also need the AI to align with our (moral) values. As discussed above in terms of ChatGPT, this is still a challenge for the GPT models and it remains to be seen what GPT-4 will be able to do in this regard.

2. Parameterization
As GPT-4 was trained with more parameters than its predecessors, it relies on more data to learn from. Therefore, it will have an improved understanding of the users intent, and will output more accurate responses.

3. Sparsity
As Altman has suggested that the size of GPT-4 won’t be increased in comparison to GPT-3, it’s unlikely that it will be using sparse models. Also, OpenAI has been relying on dense language models in the past.

4. Multimodality
Fans of automated image creation or even automated video creation, might be disappointed by GPT-4. But as Altman has hinted, GPT-4 will be a text-only model and, therefore, focus solely on language generation. As of now, it is still a daunting task to combine words and visuals.

5. Transformer Architecture
As any GPT model, GPT-4 will use transformer architecture. A transformer architecture allows for a better understanding of relationships between words in text. Hence, it allows for improved accuracy in language understanding tasks.

6. Accuracy
As GPT-4 should significantly improve in accuracy, it will carry out various tasks, such as text generation and summarization. As GPT-4 will also have more advanced capabilities for natural language understanding, it will consequently have an enhanced understanding of context or of a given task and complete it more accurately than GPT-3. GPT-4 is also designed to handle larger amounts of data and more sophisticated tasks than GPT-3.

6. Scope Of Application For GPT-4

As an improvement of GPT-3, GPT-4 will have a wide range of applications, which can be used in many different areas such as natural language processing (NLP), machine translation, speech synthesis and understanding. GPT-4 can also be used for tasks that require deep understanding of text, such as summarizing or comprehension. GPT-4's advanced algorithms allow it to perform these tasks more effectively than GPT-3 and ChatGPT.

In particular, GPT-4, as a language model, will find application in professions and businesses that need or are linked to content and content creation. In marketing and sales, GPT-4 can, for example, be used to outline and write (ad) campaigns. This also goes for writers and content creators, as they may use GPT-4 even more as a source of inspiration and help to write and generate content with GPT-4. However, as it is not always a reliable source, fact checking is always advisable.

Furthermore, with GPT-4 the content output is not scalable. Therefore, it is not suitable for cases where a lot of content is needed and where it is imperative that the given information is true and appropriate.

With a data-to-text software like AX Semantics, texts are configured only once initially in the tool, the content output scales rapidly. For e-commerce companies, data-to-text is profitable because they can, for example, very quickly generate high-quality product descriptions for hundreds or thousands of products – even in different languages. This can save time and money, as well as increase SEO visibility and conversion rates on product pages.

Do you want to know how automated content generation works?

Create your free AX Semantics account and take an interactive tour of our software!

7. What Is ChatGPT?

GPT-3 was used as a basis to build the NLP model ChatGPT, which is capable of understanding and generating human-like conversations in real time.

ChatGPT is an AI-chatbot. It’s designed to generate natural and human-like conversation responses in real-time, and is able to do so in many different languages – one at a time. ChatGPT stems from a model in the GPT-3.5 series and is described by OpenAI as a “fine-tuned” version of it.

It was designed to give users quick, precise, and helpful answers to their questions. So its main purpose is to respond to text questions in an informative or entertaining way. ChatGPT has been trained until early 2022. That means, it has great knowledge about events and developments up until this point in time and, hence, has some form of common knowledge.

Source: OpenAI

7.1 What Is The Use Of ChatGPT?

ChatGPT can be used for various conversational tasks, ranging from customer service to online dialogue recommendation. It can be used to build virtual assistants and chatbots that can generate natural conversations with humans.

It also understands and writes code in different programming languages. Therefore, the chatbot can be used for debugging codes, and can explain it as well as help to improve it. In a more general sense, ChatGPT is great at explaining complex things and issues in simple and easy to understand words.

7.2 The Limitations Of ChatGPT

As the hype over ChatGPT has been enormous, it also has its weaknesses. For example, the language model can give plausible-sounding, but incorrect or nonsensical answers. Therefore, it is strongly recommended to fact check ChatGPTs answers. And it's not likely that this issue will be fixed any time soon. As OpenAI writes on their website:

“Fixing this issue is challenging, as: (1) during RL training, there’s currently no source of truth; (2) training the model to be more cautious causes it to decline questions that it can answer correctly; and (3) supervised training misleads the model because the ideal answer depends on what the model knows, rather than what the human demonstrator knows.”

OpenAI

This is why, at least in some cases, answers can vary according to the way users formulate questions or their input phrases. This is also true, when the user gives an ambiguous inquiry. Instead of asking for clarification, ChatGPT will guess the users' intent behind the question and answer accordingly. This is very well shown in this example:

8. What Is The Future Of GPT?

GPT-4 is going to be a big step for natural language learning models. Future GPT models will likely be even more powerful, create more human-like texts, and sooner or later, will be able to produce multimodal content, as it will combine words, image, and video creation. As time goes by, the GPT models will get even better and have much larger capacity as well as greater accuracy. Tackling more and more complex tasks, such as natural language generation, machine translation, and question answering.

To become even more similar in its workings to the human brain, the AI’s role model, sparsity is another issue that will need to be addressed in the future. However, sparsity might not be so easy to achieve and the alignment problem might not be so easy to overcome.

While GPT-4 will be more powerful than GPT-3, ChatGPT is currently a good option for those looking to experiment with GPT-3 technology, and while away the time until GPT-4 will be released.

In this video, OpenAI’s CEO Sam Altman talks about the future of AI and the alignment problem in more detail:

FAQ

What is Natural Language Generation (NLG)?

Natural Language Generation (NLG) refers to the automated generation of natural language by a machine. As a part of computational linguistics, the generation of content is a special form of artificial intelligence. Natural language generation is used in many sectors and for many purposes, such as e-commerce, financial services, and pharmacy sector. It is seen to be most effective to automate repetitive and time-intensive writing tasks like product descriptions, reports or personalized content. Learn more about Natural Language Generation (NLG).

What is Content Automation?

Automated content generation with AX Semantics works with the help of Natural Language Generation (NLG) - a technology that generates high-quality and unique content on the basis of structured data that is
indistinguishable from manually written content. Text automation is used for generating product descriptions, category content, financial and sport reports or content for search engines websites. In a nutshell, it is used for all kinds of content that require large quantities and have a similar basic structure.

How Do You Use AI in Content Creation?

There are a multiple ways to use AI in content creation. You can use AI to help you come up with topics or ideas for articles, you can use it as a research tool to gather information, or it can help you to write and edit your content.
For instance, e-commerce businesses can use AI to create a chatbot that generates and displays customer service content. A chatbot is a computer program that can mimic human conversation. You can type in a topic or question, and the chatbot will respond with a list of relevant responses.
However, you can also use AI to create product descriptions, sales copies, CTAs, and more. It all depends on your business needs.

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