GPT: Generative Pre-trained Transformer

In a seminar about natural language processing and language models, the current flagship cannot be left out: GPT. While it is worthwhile having a look at the architecture of GPT itself in order to understand why it its performance has led to the fantastic advancements in the last years, this by far exceeds the scope of this seminar. Instead, we want to learn how to work with it using the OpenAI API in the next sections. However, here is at least a small (and definitely not comprehensive) overview of what GPT is and what distinguishes it from its predecessors.

A short introduction to GPT

GPT (Generative Pre-trained Transformer) is one of the current state-of-the-art language model architectures. It belongs to the family of transformer-based models, which have revolutionized NLP in recent years. What distinguishes GPT from its predecessors (and the approaches we have discussed before) is its remarkable ability to understand and generate human-like text. One key advantage of GPT over previous approaches lies in its architecture. The transformer architecture, upon which GPT is built, introduces a novel mechanism called self-attention. This mechanism allows the model to weigh the importance of different words in a sentence dynamically, capturing long-range dependencies and contextual information more effectively than earlier models like recurrent neural networks (RNNs) or convolutional neural networks (CNNs). Another factor contributing to GPT’s superiority is its scalability. By leveraging the parallelism inherent in the transformer architecture, GPT can efficiently process and learn from vast amounts of data and hence allowing it to scale to unprecedented sizes. Larger models trained on more data tend to exhibit better performance, as being exposed to a diverse range of linguistic patterns and structures during training let’s them capture more complex patterns and nuances in language. The pre-training works in a similar fashion as we’ve already seen: GPT learns to predict the next word in a sequence given the preceding context. This process enables it to capture the nuances of language across various domains and lets it generate coherent and contextually relevant text across a wide range of topics and writing styles.

What is a transformer?

The transformer architecture has been introduced in the landmark paper “Attention is All You Need” by Vaswani et al. in 2017 and revolutionized the way sequential data, like text, is processed and understood. At the heart of the transformer architecture lies the so-called self-attention mechanism, which is a novel way of capturing the relationships between different elements in a sequence, i.e., individual words or tokens in a sentence. Unlike traditional architectures, where information flow is constrained by fixed-length context windows or recurrent connections, transformers enable each word in a sentence to attend to all other words simultaneously through self-attention.

This mechanism allows transformers to dynamically weigh the importance of each word in the context of the entire sequence. This means that words that are semantically related or have a strong contextual influence on each other will receive higher attention weights, while irrelevant or less informative words will receive lower weights. By capturing these dependencies across the entire sequence, transformers excel at capturing long-range dependencies and contextual information, which is crucial for understanding and generating coherent text.

A very important detail is that the self-attention mechanism enables transformers to process input sequences in parallel rather than sequentially, leading to significant computational advantages. Unlike RNNs, which process inputs one token at a time in a sequential manner, transformers can process all tokens in parallel, making them highly efficient and scalable, especially when dealing with long sequences or large datasets. This parallelism not only speeds up training but also allows transformers to capture complex patterns and relationships in data more effectively.