Multi-head Attention

Multi-head attention is a crucial component in transformer-based models, such as BERT, GPT, and their variants. It extends the basic self-attention mechanism to capture different types of relationships and dependencies in a sequence. Here's an explanation of multi-head attention:

1. Motivation:

   • The primary motivation behind multi-head attention is to enable a model to focus on different parts of the input sequence when capturing dependencies and relationships.

   • It allows the model to learn multiple sets of attention patterns, each suited to capturing different kinds of associations in the data.

2. Mechanism:

   • In multi-head attention, the input sequence (e.g., a sentence or document) is processed by multiple "attention heads."

   • Each attention head independently computes attention scores and weighted sums for the input sequence, resulting in multiple sets of output values.

   • These output values from each attention head are then concatenated and linearly transformed to obtain the final multi-head attention output.

3. Learning Different Dependencies:

   • Each attention head can learn to attend to different aspects of the input sequence. For instance, one head may focus on syntactic relationships, another on semantic relationships, and a third on longer-range dependencies.

   • By having multiple heads, the model can learn to capture a variety of dependencies, making it more versatile and robust.

4. Multi-Head Processing:

   • In each attention head, there are three main components: queries, keys, and values. These are linearly transformed projections of the input data.

   • For each head, queries are compared to keys to compute attention weights, which are then used to weight the values.

   • Each attention head performs these calculations independently, allowing it to learn a unique set of attention patterns.

5. Concatenation and Linear Transformation:

   • The output values from each attention head are concatenated into a single tensor.

   • A linear transformation is applied to this concatenated output to obtain the final multi-head attention result. The linear transformation helps the model combine information from all heads appropriately.

6. Applications:

   • Multi-head attention is widely used in NLP tasks, such as text classification, machine translation, and text generation.

   • It allows models to capture diverse dependencies and relationships within text data, making it highly effective in understanding and generating natural language.

Multi-head attention has proven to be a powerful tool in transformer architectures, enabling models to handle complex and nuanced relationships within sequences effectively. It contributes to the remarkable success of transformer-based models in a wide range of NLP tasks.