Generative pretrained transformer (GPT) – AI tech & AI learning models

What is a generative pretrained transformer (GPT)?

A Generative Pre-Trained Transform – GPT for short – is an advanced machine learning AI model based on the Transformer architecture.

This AI technology was developed by OpenAI and is known for its ability to generate high-quality texts that are similar to those of a human being. The core of GPT technology lies in the use of large amounts of data to pre-train the model before it is fine-tuned for specific tasks.

Thus, a Generative Pre-trained Transformer (GPT) is a sophisticated AI model that is located in the world of artificial intelligence (AI) and machine learning (ML).

Developed on the basis of the Transformer architecture, which was originally introduced in 2017, GPT has the ability to generate human-like text and handle complex language understanding tasks.

At its core, GPT is designed to analyze large amounts of text data, learn from it and use this knowledge to generate text based on a given input prompt or to answer questions. This capability makes it a powerful AI tool for a wide range of AI applications, from automatic text generation and translation to advanced chatbot systems.

How does a Generative Pre-Trained Transformer – GPT work?

Basic principles of the Transformer architecture

Self-attention mechanism

The core of the Transformer architecture, and therefore also of GPT, is the self-attention mechanism. This enables the model to understand the meaning of each individual word in a sentence in the context of all the other words. Instead of stepping sequentially from word to word, as previous model architectures did, the self-attention mechanism evaluates each word simultaneously in the context of the entire sentence. This parallel approach enables more efficient processing and a deeper understanding of linguistic contexts.

An important aspect of the self-attention mechanism is its ability to recognize the relationship between widely spaced words in a text. For example, the model can understand that in the sentence “The doctor gave the patient a medicine because he was sick”, the word “he” refers to “the patient”, even if there are several words in between. By weighting the meaning of each word in relation to every other word in the text, GPT can capture subtle nuances and complex grammatical structures.

Positional Encoding

Another key element of the Transformer architecture is positional encoding. Since the self-attention mechanism does not process the input words in any specific order, the model needs a method to take into account the order of the words in the text. Positional encoding solves this problem by adding additional information to each word about its position in the sentence.

This is done by adding a position embedding to each word embedding before the data flows through the self-attention layers. The position embeddings have a unique pattern that allows the model to recognize the position of each word and take into account how far away it is from other words. This information is crucial for understanding language structures, especially in linguistic constructions where the word order strongly influences the meaning of the sentence.

By combining self-attention and positional encoding, GPT can generate and interpret text with an understanding of both context and grammatical structure, resulting in astonishingly human-like performance in various language tasks.

Pre-training process

Unsupervised learning

The pre-training process of a Generative Pre-Trained Transformer (GPT) is based on unsupervised learning, a method of machine learning in which the model is trained without explicitly labeled data. Instead of giving direct instructions on what exactly to learn, GPT analyzes an extensive corpus of text data that represents the broad spectrum of human language. This text corpus can consist of books, articles, websites and other written materials.

During the pre-training process, the model tries to recognize the structure and patterns of the language on its own. A key aspect of this is the prediction of words based on their context. For example, the model learns to predict the next word in a sentence or to complete a missing part of the text. Through this type of training, GPT learns to identify and internalize complex language patterns, syntactic structures and semantic relationships within the text.

Tokenization and embeddings

To process texts, GPT first breaks them down into so-called tokens. Tokenization is the process of converting continuous text into discrete units (tokens), which can be words, punctuation marks or even parts of words. These tokens are then converted into vectors, known as embeddings. Each embedding is a dense, high-dimensional representation that maps the semantic and syntactic properties of a token.

Embeddings enable the model to capture meanings and relationships between words. During the pre-training process, these embeddings are continuously adapted and optimized so that the model learns to represent similar words with similar vectors. This process helps the model to develop a deep understanding of the language and its nuances that goes far beyond superficial matches.

Fine tuning

Adaptation to specific tasks

After completing the pre-training process, GPT is able to cope with general language tasks. However, in order to optimize the model for specific applications, such as text generation in a specific style, translations or answering questions in a specific subject area, it is further fine-tuned. Fine-tuning is done by training the pre-trained model on a smaller, specific data set that is relevant to the desired task.

Through this additional training phase, the model learns to apply its previously acquired general language skills to the specifics of the new task. This step enables a significant improvement in performance in specific use cases, as the model can now take into account specific terminologies, styles and requirements of the target application.

Example of the fine-tuning process

The fine-tuning of GPT for the creation of legal documents could serve as a concrete example. After the model has been pre-trained on a wide range of texts, it is additionally trained on a dataset of legal texts that includes laws, judgments and contracts. During this fine-tuning phase, GPT learns to understand and apply the specific language, style and structure of legal texts.

Fine-tuning enables GPT to generate legal documents that are not only correct in terms of content, but also in line with legal spelling conventions. This adaptability makes GPT a valuable tool for a variety of specialized applications by tailoring the general model to the specific needs and requirements of each task.

Training process of a generative pre-trained transformer GPT

Generation of texts

Selecting the next word

The ability of Generative Pre-Trained Transformers (GPT) to generate text is essentially based on predicting the next word based on the given context. Once GPT receives an input text, it analyzes it and uses its comprehensive understanding of the language to determine the most likely next word. This prediction is based on the patterns, structures and relationships learned during pre-training and fine-tuning.

The process of word selection is done by calculating the probabilities of all possible next words in the vocabulary. The model weighs up how well each potential word fits in context, based on the previous words in the text. The word with the highest probability is selected as the next word and added to the generated text. This process is repeated for each new word, with the model taking into account the growing context of the already generated text.

Diversity and creativity

To ensure diversity in the generated text and to prevent GPT from always giving the same answer, techniques such as temperature and top-k sampling are used.

• Temperature: This technique controls the “creativity” of the model when selecting words. A low temperature causes the model to choose safer, more probable words, while a higher temperature causes the model to choose riskier, less predictable words. This makes it possible to fine-tune the originality of the generated text.
• Top-k Sampling: This method limits the selection of the next word to the k most probable options and then selects randomly from this reduced set. This increases the diversity of the generated texts by preventing the model from always resorting to the absolute most likely words and instead encouraging it to use more creative and varied language patterns.

GPT sample application

Let’s imagine we want to use GPT to generate a creative text on the topic of “The future of AI”. The input text (prompt) could be: “Write a short story about the role of AI in society in 50 years’ time.”

Based on this prompt, GPT starts to generate the text by selecting the next most probable word after the other. It takes into account not only the direct request, but also its understanding of topics such as technology development, social trends and possible future scenarios.

A generated text could begin like this: “By 2070, artificial intelligence had transformed almost every aspect of human life. In cities, self-driving vehicles navigated safely through the streets, while intelligent assistants made everyday life easier at home. But the biggest change was seen in the way humans and AI worked together to tackle global challenges…”

By using techniques such as temperature and top-k sampling, GPT ensures that the story is creative and diverse, and avoids reproducing predictable or stereotypical narratives. The result is a unique and captivating text that transports the reader into a possible future.

Challenges and solutions

Context limitations

One of the main problems faced by Generative Pre-Trained Transformers (GPT) is the limitation of context length, i.e. the number of words or tokens that the model can consider when generating text. Earlier versions of GPT had a fixed limit on how many previous words could be included in the calculation of the next word. This limitation has a direct impact on the model’s ability to produce long and coherent texts, as it can “forget” important contextual information from further back in time.

Solution approaches:

• Increased context length: Technical improvements and more efficient training methods have made it possible to increase the maximum context length in newer GPT versions. This enables the model to take longer text passages into account and improves the coherence of the generated content.
• Use of additional technologies: Approaches such as Recurrent Neural Networks (RNNs) or Long Short-Term Memory (LSTM) networks can help overcome context length limitations by enabling information to be “remembered” over longer stretches of text.

Distortions and ethics

Another significant problem with GPT and similar AI models are biases in the training data, which can lead to undesirable or unethical results in the generated texts. Since the training data comes from human language data, it may unintentionally reflect existing social, cultural or gender biases. This can lead to the model generating stereotypical, discriminatory or harmful content.

Solution approaches:

• Active removal of distortions: Research teams are working to clean up the training data and develop mechanisms that minimize detectable biases. This can be achieved by selectively removing or neutralizing distorted content from the training set.
• Ethical guidelines: Implementing ethical guidelines and having human moderators review generated content can help identify problematic content and prevent it from seeing the light of day.
• Transparent use: Creating a transparent environment in which users are informed about the potential and limitations of the model can contribute to ethical use. Users should be informed about the origin of the training data and the possible distortions.
• Customizable filters and policies: Developing systems that allow end users to filter or customize content based on individual or cultural preferences can also help mitigate ethical concerns.

Overall, the challenges of dealing with context limitations and biases in AI models such as GPT require a combination of technological innovation and ethical considerations. Advances in AI research combined with responsible use can help to overcome these challenges and fully exploit the positive potential of generative AI models.

History of generative pre-trained transformers (GPT) and artificial intelligence (AI)

Our compact overview of the history of Generative Pre-Trained Transformers (GPT) and artificial intelligence (AI) in general is rich in developments and breakthroughs.

Here you will find our chronology of the most important milestones:

The beginnings of AI

• 1950s: Alan Turing publishes the paper “Computing Machinery and Intelligence”, in which he poses the question “Can machines think?” and introduces the Turing test.
• 1956: The term “artificial intelligence” is introduced at the Dartmouth Conference, which is generally regarded as the birth of AI research.

Developments before GPT

• 1980s: The development of neural networks begins with the backpropagation algorithm, which enables the training of deep neural networks.
• 1997: IBM’s Deep Blue beats the reigning world chess champion Garry Kasparov.
• 2012: AlexNet, a deep neural network, wins the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), marking a turning point for deep learning and AI.

The era of GPT and OpenAI

• December 2015: OpenAI is founded as a research company in the field of Artificial Intelligence with the aim of promoting and developing friendly AI to benefit humanity as a whole.
• June 2018: OpenAI releases GPT (Generative Pretrained Transformer), the first version of its revolutionary model. GPT demonstrates impressive capabilities in text generation and sets new standards for language models.
• February 2019: OpenAI presents GPT-2, an improved version with 1.5 billion parameters. Due to concerns about possible misuse, the full version will not be published for the time being. GPT-2 shows a clear improvement in text coherence and versatility.
• June 2020: GPT-3 is released with an architecture of an impressive 175 billion parameters. This model sets new standards for natural language generation and is used in a wide range of applications, from automatic text generation to program code creation.
• 2021 and beyond: While GPT-3 continues to find widespread use, OpenAI is working on the next generation of AI models as well as refining their applications, ethical guidelines and accessibility issues. The exact date for the release of a new GPT version is uncertain, but research and development is continuing.

Influence and significance

The development of GPT by OpenAI has revolutionized AI research and the application of artificial intelligence in many areas. By providing powerful, flexible and increasingly accessible models for natural language generation, OpenAI has helped to push the boundaries of what is possible with AI. The ongoing focus on ethical considerations and the safety of AI systems also underlines the responsibility that comes with advanced AI technology.

Our chronology gives you a quick overview of the most important milestones in the development of GPT and AI in general, showing the rapid progress in this field and the role of research institutions such as OpenAI in shaping the future of artificial intelligence.