Difference between Self-Attention and Multi-head Self-Attention

Self-attention and multi-head self-attention are both mechanisms used in deep learning models, particularly transformers, to understand the relationships between elements in a sequence.

Here’s the breakdown of their key differences:

Self-Attention:

• Core concept: Analyzes how each element in a sequence relates to all other elements.
• Process: Each element in the sequence is transformed into three vectors: Query (Q), Key (K), and Value (V).
• A compatibility score is calculated between each pair of elements using the Q and K vectors.
• These scores are normalized using softmax to create attention weights, indicating how much “attention” each element should pay to others.
• The attention weights are used to weight the V vectors, essentially creating a context-aware representation for each element based on its relationship with others.

Multi-Head Self-Attention:

• Builds upon self-attention: Performs multiple self-attention operations in parallel, with each operation learning to focus on different aspects of the relationships between elements.
• Process (similar to self-attention but in parallel):
• The input is projected into multiple sets of Q, K, and V vectors for each “head.”
• Separate attention scores and weighted outputs are calculated for each head.
• The outputs from all heads are concatenated to form the final output.

Analogy:

Imagine you’re reading a sentence. Self-attention would be like considering how each word relates to every other word to understand the overall meaning. Multi-head self-attention would be like reading the sentence several times, each time focusing on a different aspect like grammar, word relationships, or sentiment. By combining these focused readings, you get a richer understanding of the sentence.

Key takeaway:

• Self-attention provides a context-aware representation for each element in a sequence.
• Multi-head self-attention refines this by allowing the model to learn different aspects of the relationships between elements, leading to a more robust understanding of the sequence.

How self-attention and multi-head self-attention are used in transformers

In Transformers, both self-attention and multi-head self-attention are crucial for understanding the long-range dependencies within sequences. Here’s how they work with a real-world example:

Scenario: Imagine you’re writing a news article about a scientific discovery. The first sentence is: “Scientists discovered a new method to generate clean energy.”

Self-Attention:

1. Tokenization: The sentence is broken down into smaller units called tokens (words in this case).
2. Embedding: Each token is converted into a vector representation.
3. Self-attention magic: Self-attention comes into play here. For each token’s vector (let’s say for “discovered”), it calculates how relevant other tokens (“scientists,” “new method,” “clean energy”) are to understanding “discovered.”

• It does this by creating three vectors for each token: Query (what information do I need?), Key (what information can I provide?), and Value (the actual information I hold).
• The model then calculates a score for each pair of tokens based on how well their Query and Key vectors match.
• Higher scores indicate a stronger relationship.

1. Context building: Using these scores, the model attends more to relevant tokens. So, for “discovered,” it might pay more attention to “scientists” and “new method” to understand the context of the discovery.

Multi-Head Self-Attention:

This builds on top of self-attention by performing multiple self-attention operations in parallel, each focusing on slightly different aspects:

• Multiple Heads: Imagine having multiple researchers analyzing the sentence.
• Head 1 (Grammar): This head might focus on grammatical relationships, giving high scores to “discovered” and “scientists” for subject-verb agreement.
• Head 2 (Word Relationships): This head might focus on how words connect, giving high scores to “discovered” and “new method” for the action and its innovation.
• Head 3 (Entity Recognition): This head might identify named entities, giving high scores to “scientists” and potentially “clean energy” (if recognized as a field).

Combining Heads:

The outputs from all these heads (understanding grammar, word relationships, entities) are combined. This gives the model a richer understanding of the sentence, allowing it to not only understand the core idea but also recognize grammatical structure, relationships between concepts, and potentially identify key entities.

Benefits:

By using self-attention and multi-head self-attention, transformers can effectively capture long-range dependencies in sequences like sentences, code, or even protein structures. This allows them to perform various tasks like machine translation, text summarization, and anomaly detection in complex data.

Conclusion

In conclusion, self-attention and multi-head self-attention are powerful techniques used in transformers to analyze relationships within sequences. Self-attention allows each element in a sequence to understand how it connects to all others, building a context-aware representation. Multi-head self-attention takes this a step further by performing self-attention in parallel with multiple “heads,” each focusing on different aspects of the relationships. This combined analysis leads to a richer and more robust understanding of the sequence.

These techniques are fundamental to the success of transformers in various tasks like machine translation, where understanding the relationships between words across languages is crucial. As deep learning continues to evolve, self-attention and multi-head self-attention are likely to play a significant role in unlocking new capabilities for analyzing and processing complex sequential data.

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