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Understanding GPT: Generative Pre-trained Transformer Models

Olaf Holst • January 8, 2025

You might have heard the term “GPT” thrown around in discussions about AI. But what exactly is it, and why is it such a big deal? In simple terms, GPT stands for Generative Pre-trained Transformer, and it’s a groundbreaking way for machines to understand and produce human-like text. Created by OpenAI, GPT models have become increasingly popular for tasks involving language, like writing, summarizing information, or even powering chatbots.

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What Is GPT?

GPT is “generative” in that it can create text that sounds almost as if a human wrote it. It’s also “pre-trained,” meaning it’s fed massive amounts of text from sources like the internet and books before being fine-tuned for specific tasks, such as writing product descriptions or answering questions. Finally, it uses a “transformer” design—a neural network framework that leverages an attention mechanism to process words simultaneously rather than one by one. This setup helps GPT handle large amounts of text quickly and understand context more effectively than older AI models.

A Brief History of GPT

The story of GPT (Generative Pre-trained Transformer) began in 2018 when OpenAI introduced the world to this language model through the paper titled “Improving Language Understanding by Generative Pre-training.” This model marked a significant shift in the field of natural language processing (NLP). Unlike traditional machine learning models that relied heavily on labeled datasets, GPT demonstrated the power of pre-training on an enormous amount of unlabeled text gathered from the internet. By learning the patterns and structures of language at scale, GPT became a general-purpose model capable of being fine-tuned for a variety of specific tasks.

The release of GPT set the stage for a new era of AI innovation, but it was only the beginning. In 2019, OpenAI unveiled GPT-2, a larger and more powerful version that further demonstrated the potential of generative models. GPT-2 could generate coherent and contextually relevant text, sparking excitement—and some ethical debates—about the implications of such advanced AI capabilities. Its potential to create realistic-sounding text raised questions about the potential misuse of AI for misinformation, leading OpenAI to take a cautious approach to its initial public release.

By 2020, OpenAI introduced GPT-3, a model so versatile and powerful that it became a household name in AI discussions. With 175 billion parameters, GPT-3 was significantly larger than its predecessor and capable of tasks that seemed almost magical—drafting emails, composing poetry, creating art descriptions, assisting with coding, and even engaging in philosophical debates. What set GPT-3 apart was its ability to maintain a natural, human-like conversational style, blurring the lines between human and machine-generated text.

How Does GPT Work?

GPT is built on the transformer architecture first outlined in the paper “Attention Is All You Need.” Its secret weapon is an attention mechanism that looks at all words in a sentence or paragraph in parallel, helping the model figure out the best possible next word based on context. GPT specifically uses the “decoder” part of the transformer to generate text, constantly referencing the conversation or prompt as it constructs a response. This parallel processing approach allows GPT to produce coherent, context-aware answers at remarkable speed.


By combining this powerful architecture with vast amounts of text data, GPT has become one of the most impressive AI models for creating human-like text. Its influence is clear across many industries, from customer service chatbots to writing assistants—and it’s only growing.

GPT in-depth FAQ

  • What Is Unsupervised Pre-Training

    During this phase, the model is trained on a large amount of text without specific guidance on what it needs to learn. It uses a variation of the language modeling task where it predicts the next word in a sentence given the previous words, thereby learning a probability distribution over the word sequence. This process allows the model to develop a deep understanding of language structure, grammar, and contextual relationships between words.


    The key characteristic of unsupervised pre-training is that it does not rely on labeled data. Instead, the model learns patterns, semantics, and syntactic features from the raw text. By processing a vast and diverse corpus, the model acquires general knowledge that can be fine-tuned later for specific tasks, such as sentiment analysis, question answering, or machine translation.


    This phase is crucial for modern language models as it forms the foundational understanding upon which task-specific knowledge is built during subsequent supervised fine-tuning or instruction tuning. Unsupervised pre-training significantly reduces the need for extensive labeled datasets, making it a cost-effective and scalable method for building advanced AI systems.

  • What is Supervised Fine-Tuning

    After the initial unsupervised training, the model undergoes supervised fine-tuning to tailor its capabilities for specific tasks. This phase involves training the model on a smaller, curated, task-specific dataset, which provides labeled examples of input-output pairs relevant to the desired application.


    During supervised fine-tuning, the model's weights are adjusted to align its predictions with the correct outputs provided in the dataset. This phase refines the broad, general-purpose knowledge gained during unsupervised training and helps the model perform tasks such as:


    • Translation: Converting text from one language to another with high accuracy and fluency.
    • Summarization: Condensing long documents into concise summaries while preserving key information.
    • Question-Answering: Responding to specific queries based on a given context or knowledge base.
    • Sentiment Analysis: Identifying the emotional tone or sentiment of a piece of text.
    • Text Classification: Categorizing text into predefined groups, such as spam detection or topic labeling.

    Supervised fine-tuning is critical because it customizes the model to better meet the needs of particular use cases. The quality and diversity of the labeled dataset used in this phase greatly influence the model’s performance, as it learns to handle domain-specific nuances and constraints.


    This process often employs techniques like transfer learning, where the pre-trained model serves as a starting point, enabling quicker training and requiring less labeled data compared to training a model from scratch.

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