93. GPT: The Model That Predicts the Next Word Forever
The article discusses the capabilities of the GPT model, which predicts the next word in a sequence. It highlights the differences between GPT and BERT, emphasizing GPT's autoregressive nature. The piece also outlines the training process and the significance of various sampling methods in text generation.
- ▪GPT reads text from left to right and predicts the next token based on previous ones.
- ▪The model learns grammar, facts, and reasoning patterns through its training on vast amounts of text and code.
- ▪Each version of GPT has progressively improved in its ability to generate coherent text.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 1358056) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Akhilesh Posted on May 21 93. GPT: The Model That Predicts the Next Word Forever #ai #productivity #beginners #python BERT reads everything at once and understands. GPT reads left to right and predicts what comes next. Forever. That difference sounds limiting. It's not. When you train a decoder-only transformer on billions of tokens of text and code, predicting the next word forces the model to learn grammar, facts, reasoning patterns, writing styles, and more.
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