Improving Language Understanding by Generative Pretraining

We’ve reached a pivotal moment in our journey—after exploring how BERT, an encoder-only model, revolutionized language understanding by reading text in both directions, we now arrive at the moment when generative AI truly came into being. While BERT excels at comprehending and classifying text, it was never built to create a new language. Enter GPT—Generative Pre-trained Transformer—a model that flips the script entirely using a decoder-only architecture designed to understand language and generate it. This shift marks a dramatic evolution from models that simply get language to those that can actively create it, opening the door to the modern era of conversational agents, creative writing tools, and more.

What is GPT?

GPT was introduced by OpenAI in the groundbreaking paper Improving Language Understanding by Generative Pre-Training. https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdfDeveloped by researchers including Alec Radford and Ilya Sutskever, who were also responsible for scaling up the encoder-decoder architecture we discussed earlier, GPT emerged as a bold experiment to harness the power of transformers for language generation. Unlike BERT, which focuses on understanding text using an encoder-only approach, GPT uses a decoder-only architecture. This design is optimized for predicting what comes next in a sequence, making it ideally suited for generating coherent, flowing text.

Imagine reading a story where you try to predict how the narrative continues instead of merely summarizing what you’ve read. That’s precisely what GPT does: it takes the text that’s already been written, analyzes it, and then generates new words that logically follow, word by word. This ability allows GPT to excel at tasks such as text completion, creative writing, and maintaining a conversation. In effect, GPT isn’t just a reader—it’s a storyteller.

Historically, OpenAI’s pursuit of GPT was fueled by the desire to scale up the transformer architecture to handle understanding and generation. The team recognized that if you could train a model on vast text data to predict the next word, you could harness this predictive power to create text that sounds remarkably human. GPT-1 was trained on BookCorpus, a dataset of 7,000 books, proving that transformers could outperform RNNs in language modeling. Building on the success of the original GPT, OpenAI released GPT-2 and GPT-3, each larger and more powerful than the last.

OpenAI’s researchers, led by Alec Radford, built on insights from the transformer community and pushed the envelope further, demonstrating that a decoder-only model could ...