Unveiling the Power of Large Language Models with Princeton NLP Experts
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In this thrilling episode, Alexander and Amit, two brilliant minds from the Princeton NLP group, delve into the fascinating world of building large language models from the ground up. These models are the very backbone of the revolutionary Chachi GPT, a technology that would be unimaginable without the groundwork laid by these massive language models. They take us on a journey through tokenization, where words are transformed into numbers, and subword tokenization, a clever method to handle the vast and unpredictable space of words in natural language.
The dynamic duo introduces us to the concept of word embeddings, powerful vectors that allow similar words to be grouped together in a high-dimensional space. These embeddings enable models to learn representations with remarkable characteristics, shaping the way language is understood and processed by machines. As they unravel the mystery behind large language models, they shed light on the transformative Transformer architecture, a neural network design that has revolutionized the field of natural language processing.
Self-attention emerges as a crucial operation within Transformers, facilitating the seamless integration of information from past tokens into current predictions. Meanwhile, the feed-forward layers play a pivotal role in mapping embeddings to predict the next token, storing essential factual knowledge along the way. With each layer in models like GPT3 consisting of about 100 layers, the flexibility and adaptability of these models to handle vast amounts of data and make complex predictions are truly awe-inspiring. Join Alexander and Amit as they unlock the secrets of language modeling and take us on a thrilling ride through the cutting-edge world of NLP.
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Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
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