From GPT-1 to GPT-4: A Comprehensive Analysis and Comparison of OpenAI’s Evolving Language Models

OpenAI provides a wide selection of models, each with its own features and cost structure, to meet the needs of various applications. Models are regularly updated to reflect the most recent advances in technology. Users can also adjust the models to make them work better for them. OpenAI’s GPT models have allowed major natural language processing (NLP) advancements.

Simply put, what is GPT?

One machine learning model for NLP applications is the Generative Pre-trained Transformer (GPT). These models are pre-trained on large volumes of information, such as books and websites, to produce natural-sounding, well-structured text.

More simply, GPTs are computer programs that can generate text that looks and reads like a human being wrote it but was not designed to do so. That makes them malleable for NLP applications such as question answering, translation, and text summarization. Regarding natural language processing, GPTs are a major step forward since they enable machines to comprehend and generate language with unparalleled fluency and accuracy. The four GPT models, from the original to the most recent GPT-4, are discussed below, along with an analysis of their strengths and weaknesses.

GPT-1

In 2018, OpenAI unveiled GPT-1, the first iteration of a language model built on the Transformer architecture. Its 117 million parameters were a huge leap forward from even the most advanced language models of the time.

GPT-1’s capacity to produce natural, intelligible speech in response to a prompt or context was one of its many capabilities. The Common Crawl, a vast dataset of web pages containing billions of words, and the BookCorpus dataset, a collection of more than 11,000 books on various topics, were used to train the model. GPT-1 was able to hone its language-modeling skills with the help of these varied datasets.

GPT-2

OpenAI published GPT-2 in 2019 to replace GPT-1. It was significantly larger than GPT-1, with 1.5 billion parameters. By fusing Common Crawl with WebText, a considerably larger and more varied dataset was used to train the model.

GPT-2’s capacity to construct logical and plausible text sequences was one of its strengths. Its ability to mimic human reactions also makes it a useful resource for various applications in natural language processing, including content generation and translation.

However, GPT-2 does have certain drawbacks. Complex reasoning and contextual understanding took a lot of work for it. However, GPT-2 struggled to keep longer passages coherent and in context, despite its superior performance on shorter ones.

GPT-3

The release of GPT-3 in 2020 ushered in a period of exponential growth for models of natural language processing. The size of GPT-3, at 175 billion parameters, is more than ten times that of GPT-2 and one hundred times that of GPT-1.

BookCorpus, Common Crawl, and Wikipedia are just a few sources used to train GPT-3. GPT-3 can produce high-quality results on various NLP tasks with roughly a trillion words across the datasets with little to no training data.

GPT-3’s capacity to compose meaningful prose, write computer code, and create art is a major advancement over earlier models. Unlike its predecessors, GPT-3 can interpret the context of a text and come up with relevant responses. Chatbots, original content generation, and language translation are just a few of the many uses that could benefit greatly from the capacity to generate text that sounds natural.

Concerns concerning the ethical implications and potential misuse of such potent language models were also highlighted in light of GPT-3’s powers. Many professionals are concerned that the model could be misused to create harmful content like hoaxes, phishing emails, and viruses. Criminals have been using ChatGPT to develop malware.

GPT-4

The fourth generation GPT was released on March 14, 2023. It’s a huge improvement over the GPT-3, which itself was revolutionary. Even though the model’s architecture and training data have yet to be made public, it is clear that it improves over GPT-3 in key respects and addresses some of the shortcomings of the prior iteration.

ChatGPT Plus subscribers have unlimited access to GPT-4, but only for so long. Joining the GPT-4 API waitlist is another option, albeit it could be a while before you get access. Nonetheless, Microsoft Bing Chat is the quickest access point for GPT-4. There is no cost or waiting list to participate.

The GPT-4’s ability to function in multiple modes is a defining characteristic. This allows the model to take a picture as input and treat it like a text prompt.

Modeling in OpenAI

One set of AI systems built to comprehend and produce natural language is OpenAI’s GPT-3 models. Although the more advanced GPT-3.5 generation models have replaced these models, the original GPT-3 base models (Da Vinci, Curie, Ada, and Babbage) are still available for customization. Due to its merits, each model is best suited to a certain set of applications.

• Davinci is the most advanced model in the GPT-3 family and can perform any work its siblings can. It was built for demanding jobs requiring an in-depth grasp of context and complexity. But unlike the other models, the computational cost of this great capability is higher.
• Curie: This model has the same high level of functionality as Da Vinci but at a lower price and significantly higher operating speed. It is a good option for many jobs since it finds a happy medium between power and efficiency.
• Ada: Ada was created for elementary programming jobs. It’s the most affordable and fastest of the GPT-3 models. Ada can be cost-effective if the job doesn’t need extensive contextual expertise.

When it comes to simple things, Babbage can handle them. It’s incredibly quick and cheap, just like Ada. It excels in jobs when speed and efficiency are prioritized over in-depth comprehension.

These models were trained on data through October 2019, and their maximum token capacity is 2,049. The task’s complexity, desired output quality, and available computational resources all play a role in determining which model to use.

So why do we need so many variants?

A selection of models allows us to meet the requirements of a diverse set of customers and scenarios. Using a more capable model than necessary can incur unnecessary computing costs, and not all activities necessitate the highest capacity level. OpenAI provides a variety of models to its customers, each with its own set of strengths and weaknesses, as well as its price tag.

Utilization and storage of data

Data privacy is important to OpenAI. Unless users opt-in, the OpenAI API will no longer use user data for model training or improvement as of March 1, 2023. Except for cases where the law mandates retention, API data will be erased after 30 days at the latest. Zero data retention might be an option for high-trust consumers who use particularly sensitive applications.

OpenAI’s Present Models

OpenAI’s models are varied, each built for a particular purpose. Some of the models are briefly described below.

• The GPT-4 Limited Beta is an enhanced version of the GPT-3.5 series that can read and write computer code and plain language. It’s still in the beta testing phase, and only select users have access now.
• The GPT-3.5 series of models can interpret and produce code in natural language. The get-3.5-turbo is this family’s most powerful and cost-effective member, and it excels at conversation while still performing well on more conventional completion tasks.
• DALLE Beta: This methodology combines visual creativity with language comprehension to develop and edit graphics responding to a natural language challenge.
• Whisper is a beta voice recognition model that can transcribe spoken words into written ones. Multilingual speech recognition, translation, and identification are possible because of their training on a large and varied dataset.
• Embedding models translate text into a numerical representation to perform tasks such as search, clustering, recommendation, anomaly detection, and classification. Safe and courteous spaces can be maintained with the help of this model, which is trained to identify potentially problematic text.
• GPT -3: This series of models is capable of both comprehending and producing natural language. Although the more powerful GPT-3.5 versions have replaced the original GPT-3 base models, they are still available for customization.

OpenAI promises regular updates to its models. There have been consistent updates to some models recently, like the gpt-3.5-turbo. Once a new version of a model is released, the previous version remains supported for at least three months to accommodate developers who desire stability. OpenAI is a versatile platform due to its extensive library of models, regular updates, and emphasis on data protection. OpenAI offers a model that can detect sensitive information, convert audio to text, and generate natural language.

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References:

• https://www.makeuseof.com/gpt-models-explained-and-compared/
• https://www.geeky-gadgets.com/openai-models/?utm_source=flipboard&utm_content=topic%2Fmachinelearning