Large language models (LLMs) are making drastic changes in artificial intelligence (AI) research and natural language processing (NLP) by allowing machines to interpret and produce content that resembles human like never before! One of these models is notable for being a turning point in the progress of AI language production. It is the Generative Pre-trained Transformers (in short, GPT) series.
Developed by OpenAI, a Microsoft backed research organization, the GPT models are pre-trained on gigantic volumes of data that enable them to produce human-like language while doing a number of tasks like generating and summarizing text, answering questions, and even understanding images. Every model from GPT-2 to GPT-4 has seen an obvious upgrade in terms of training data, architecture, and performance.
In this blog, we will find out how large language models evolved from GPT-2 to the latest GPT-4, focusing on the modifications and new features that each iteration offered, particularly in the field of content production.
Read: Large Language Model (LLM) Application Development
- Evolution of Large Language Models
It all started when OpenAI unveiled its first-ever GPT model, GPT-1, in 2018!
The model consisted of 117 million parameters, which set the base architecture for ChatGPT. It was trained on a massive collection of text data scraped from books available online and fine-tuned for tasks like summarization, question-answering, and text completion. Using this model, it was easy to translate languages, paraphrase and create fresh content, and ask common queries.
Still, its performance was not robust enough for several practical use cases. Besides, it couldn’t grasp and produce lengthy and complex texts. That said, GPT-1 exhibited an outstanding capability to generate language that is harmonious and relevant to the situation, which set the stage for further developments.
- GPT-2
GPT-2 was a major update introduced in February 2019 that featured 1.5 billion parameters (ten times more than its predecessor!) with architecture based on the transformer model.
The model showed a substantial upgrade in its text production abilities. It generated logical, multi-paragraph writing, like long articles and stories without any grammar mistakes. This increased its usefulness for a good range of NLP tasks, from translation to summarization and question-answering. It’s where the GPT revolution began!
In addition, to make tweaks to language models and make sure that their replies remain logical, consistent, and relevant, GPT-2 was put through a process called ‘Modified Objective Training’. This involved adding extra contextual elements, such as ‘Parts of Speech’.
Nevertheless, it had a few drawbacks. GPT-2 didn’t give accurate information and produced extremely convincing writing. So, there were fears that people might exploit it to create false, unpleasant, or prejudiced content.
- GPT-3
Then, June 2020 marked the beginning of GPT-3, the third iteration of the GPT series! The model boasted 175 billion parameters – over ten times the number of parameters in GPT-2.
Compared to GPT-2, GPT-3 is a more advanced language model that underwent training on more than 570 GB of text data. The resources covered articles, websites, books, and programming code. For this reason, it was able to reply to many kinds of commands and inquiries and still can!
Honestly, GPT-3 is a game changer! It has a knack for producing text in a human way for activities, such as composing music, writing jokes, crafting emails and product descriptions, and creating programming code. Moreover, it can translate languages and answer factual questions. Due to its marvelous capabilities, GPT-3 enticed a lot of interest. Today, businesses and industries from education to healthcare are using it to create content and improve customer service.
On the flip side, GPT-3 lacked reliability and fairness. It provided incorrect and irrelevant answers and occasionally produced rude or one-sided text.
- GPT-4
Released in March 2023, GPT-4 is the fourth large language model that powers Microsoft’s new Bing and other third-party apps. There were a staggering 500 billion to 1 trillion parameters used to train this latest model.
GPT-4 aims to fix some of the problems with its predecessors, like factual inaccuracies and skewed or wounding output. It promises to generate more relevant and safer responses. Absorbing and producing text in other languages is one of GPT-4’s goals as well.
This futuristic and reliable model can analyze and produce 25000 words! It excels at understanding and describing pictures, comprehending jokes, writing codes in diverse programming languages, completing incomplete sentences, and co-relating to prompts. Further, it features improved fine-tuning mechanisms, so it can better adapt to particular tasks and industries.
However, like its past versions, GPT-4 also doesn’t have real-time internet connectivity, yet enjoys the advantages of a larger, more varied training dataset.
- GPT-4o
In May 2024, OpenAI launched GPT-4o, an optimized iteration of GPT-4. Despite being based on GPT-4, its improvements allow it to operate more quickly and effectively than the GPT-4 model itself.
GPT-4o pushes the limits of AI’s ability to grasp and produce writing that is similar to that of a human, signaling a historic milestone in language modeling. It provides clearer and more concise responses, which makes it excellent for academic and professional use. Also, it offers well-organized explanations that improve intelligibility, especially in technical and scientific contexts.
Furthermore, GPT-4o can brilliantly write creative content and deliver highly artistic and compelling writing. It is a useful tool for developers since it can produce extensive code snippets. Moreover, scholars and students can benefit from its more in-depth and perceptive analysis of literature.
Due to its extensive applications, improved natural language understanding, lower bias, and higher efficiency, GPT-4o boasts the power to make noteworthy changes in ample industries, chiefly, education, research, marketing, content creation, and customer service.
Even though GPT-4o uses the most expansive and current training data from the internet, it maintains the tradition of not being able to access the internet.
Read: ChatGPT 4o Vs. Previous Versions: What’s New and Improved
- Closing Thoughts
To put it concisely, AI technology has made tremendous strides from GPT-1 to GPT-4! The features and advancements that each iteration has brought about have expanded AI’s potential across a range of industries. With every step forward, we get closer to a time when artificial intelligence permeates every part of our lives and improves our interaction, ingenuity, and efficiency.
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