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Attention in transformers, visually explained | Chapter 6, Deep Learning

3Blue1Brown・25 minutes read

Transformers are essential in modern AI, allowing for rich contextual understanding in language models by refining embeddings through attention mechanisms and multi-headed approaches to process text efficiently and accurately. In GPT-3, there are 96 attention heads in each block, resulting in around 600 million parameters per block, contributing to the network's total 175 billion parameters.

Insights

  • Transformers utilize attention mechanisms to refine word embeddings, enabling contextual understanding and the prediction of the next word in a text through high-dimensional vectors.
  • Multi-headed attention in models like GPT-3 involves running numerous parallel attention heads, each with its own key, query, and value matrices, collectively contributing to the vast number of parameters in the network.

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Recent questions

  • What is the purpose of transformers in AI?

    Transformers are essential in modern AI tools for predicting the next word in a text by associating tokens with high-dimensional vectors, enabling rich contextual understanding.

  • How does the attention mechanism refine embeddings in transformers?

    The attention mechanism in transformers refines embeddings by moving information between them, allowing for encoding broader contextual meanings beyond individual words based on relevance and alignment.

  • What is the significance of the attention pattern in language models?

    The attention pattern assigns weights to relevant words by measuring alignment and relevance through dot products between keys and queries, impacting the scalability of context windows in language models.

  • How are embeddings updated in transformers using attention mechanisms?

    Embeddings in transformers are updated by multiplying a value matrix with the embedding of a word, resulting in a value vector that is added to another word's embedding, reflecting a more contextually rich meaning.

  • What is the role of multi-headed attention in transformer models?

    Multi-headed attention in transformer models involves running multiple attention heads in parallel, each with its own key, query, and value matrices, contributing to the overall parameters and efficiency of the network.

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Summary

00:00

"Transformers in AI: Enhancing Contextual Understanding"

  • Transformers are crucial in large language models and modern AI tools, originating from the 2017 paper "Attention is All You Need."
  • The model's goal is to predict the next word in a text by breaking it into tokens and associating each with a high-dimensional vector.
  • Directions in the embedding space correspond to semantic meanings, allowing for rich contextual understanding.
  • The attention mechanism in transformers refines embeddings to encode broader contextual meanings beyond individual words.
  • Attention enables the model to move information between embeddings, refining word meanings based on context.
  • The attention block consists of query and key matrices that map embeddings to smaller spaces to identify relevant connections.
  • Dot products between keys and queries measure relevance, with larger values indicating alignment and relevance.
  • Normalizing these values through softmax creates an attention pattern, assigning weights to relevant words.
  • During training, masking prevents later tokens from influencing earlier ones in the attention pattern.
  • The attention pattern's size is equal to the square of the context size, impacting the scalability of context windows in language models.

13:22

Attention Mechanisms in Transformer Models: A Summary

  • The process of using attention mechanisms involves adjusting embeddings of words to reflect their relevance to other words in a high-dimensional space.
  • To implement this, a value matrix is multiplied by the embedding of a word, resulting in a value vector that is added to the embedding of another word.
  • The value vectors are associated with corresponding keys and are multiplied by weights to update embeddings.
  • The updated embeddings reflect a more contextually rich meaning, such as that of a fluffy blue creature.
  • This process is repeated across all columns, producing a sequence of refined embeddings.
  • Each attention head in a transformer model is parameterized by key, query, and value matrices, with a total of 6.3 million parameters for one head.
  • Multi-headed attention involves running multiple attention heads in parallel, each with its own key, query, and value matrices.
  • In GPT-3, there are 96 attention heads in each block, resulting in around 600 million parameters per block.
  • The value map is factored into two smaller matrices, the value down and value up matrices, to efficiently manage parameters.
  • The total number of key, query, and value parameters in GPT-3 amounts to just under 58 billion, contributing to the network's overall 175 billion parameters.
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