Improving Language Understanding by Generative Pretraining
Explore the principles of generative pretraining with GPT models, understanding how they generate text by predicting the next word in a sequence. Learn how the decoder-only transformer architecture powers language generation, enabling applications like creative writing, dialogue, and conversational AI. This lesson helps you grasp the evolution from understanding to generation in NLP and how GPT’s training methods led to breakthroughs in human-like text creation.
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We’ve reached a pivotal moment. BERT showed how an encoder-only model could revolutionize language understanding by reading text in both directions. But BERT was never built to create. Enter GPT, generative pre-trained transformer, a decoder-only model that flips the script. Instead of just understanding text, GPT predicts what comes next, turning transformers into storytellers and paving the way for modern generative AI.
What is GPT?
Introduced by OpenAI in the paper Improving Language Understanding by Generative Pre-Training, GPT was designed to harness the transformer decoder for text generation. Unlike BERT, which summarizes sentences, GPT predicts the next word in a sequence, one token at a time.
Think of it as reading a story and trying to guess how it continues. GPT excels at completing text, generating dialogue, and writing creatively because its design is optimized for continuation rather than classification.
OpenAI’s early experiments (GPT-1 trained on BookCorpus) proved this approach could outperform RNNs. Later versions, such as GPT-2, GPT-3, and beyond, scaled ...