#75 Pre-training: When Transformers Pay Attention
In the world of training large language models, one crucial step stands out: pre-training. This process forms the foundation of building powerful language models and sets the stage for their subsequent fine-tuning. In this article, we will embark on an exciting journey into the realm of pre-training, where language models come alive with the magic of text prediction and contextual understanding.
Pre-training: Unleashing the Power of Language Models
The fodder for pre-training is text corpus or what Karpathy calls "compressing the internet". Once the text corpus is gathered, the game begins. We start by tokenizing the text, converting each word into its numerical representation. Imagine the sentence "when it rains it pours" transformed into its tokenized form, "when it rains it pours." Now, here comes the fun part - we want our language model to predict the next word after "when it rains it."
During pre-training, the language model morphs into a virtual mind reader, predicting what comes next in a sentence - a process we call 'token prediction'. With a touch of black magic, it predicts multiple tokens and assigns probabilities to each one. The most probable token emerges from the shadows, revealing itself as the chosen prediction. But how does our language model know? Ah, the labeled data comes to the rescue, whispering the correct answer: "pours." The model's training progresses as it learns to minimize the gap between its predictions and reality, perfecting the art of language understanding.
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The Power of Attention: Transformers and Language Wizardry
Now, let's unravel the secrets of transformers, the enchanting wizards of language processing. Unlike their sequential counterparts, the wizards of the stone age, such as Recurrent Neural Networks (RNNs) and Long Short-Term Memory networks (LSTMs), transformers wield a superpower we all yearn for - attention (an elusive trait in the hustle-bustle of the modern world).
Picture this: "when it rains it ?" How would a transformer unravel this mysterious sentence? Like humans or unlike traditional networks, transformers don't play by the rules of sequential analysis. They break free from convention and embrace the whole context in one magnificent sweep. They pay attention to every word, pondering the relationships and hidden connections within "when it rains it." With this holistic understanding, transformers work their spellbinding magic, predicting the next token with uncanny accuracy.
Armed with a captivating attention mechanism, transformers assign varying weights to each word, illuminating the most significant parameters to aid precise predictions. They embark on a forward pass, conjuring predictions using these weighted representations. And in the realm of the backward pass, adjustments are made based on the calculated loss, strengthening the model's abilities with each iteration. It's a grand spectacle of leaps of faith, measured gaps, and adjustments made with unwavering conviction.
Conclusion
Pre-training stands as a vital step in training large language models, unlocking their potential for awe-inspiring text prediction and contextual understanding. Through the captivating process of pre-training, language models learn to read our minds, complete sentences, and unravel the mysteries of human language. With the arrival of transformers and their enchanting attention mechanism, the world of language models has been forever transformed. So, let us venture forth, embracing the magic of pre-training, as we witness the wondrous evolution of intelligent language models.