What is Attention in Transformers?
The attention mechanism is a core and innovative idea that empowers transformer models. Attention helps the model represent semantic relationships in data sequences, while also enabling parallel processing. Similar to hidden states in RNNs, attention allows for parallel processing. Attention helps the model understand the context and relationships between words in a sentence. The attention process includes self-attention for each token, where the key points of other tokens are used to compute the attention score for the current token. Multi-head attention is used to model various features or contexts of the sentence and its tokens.
Quantitative Values and Names of Theories:
Attention Mechanism:
Pros: Represents semantic relationships between tokens in a sequence. Allows for parallel processing, speeding up training and prediction. Helps the model understand context and relationships between words in a sentence. Cons: Requires significant computational resources. Requires training a large number of parameters.
Self Attention:
Pros: Represents relationships between tokens in a sentence. Allows the model to understand the context of a word and its relationship with other words. Cons: Requires significant computation when processing long sequences.
Multi-Head Attention:
Pros: Models various features or contexts of the sentence and its tokens. Enhances the ability to model complex relationships in the data. Cons: Requires more computational resources compared to self-attention.
Theory Names:
Attention Mechanism: “The Key to Understanding Sequence Relationships” Self Attention: “Unveiling Token Contexts: The Power of Self Attention” Multi-Head Attention: “Capturing Diverse Contexts: The Multi-Faceted Approach”
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