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:
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.
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.
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.
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.
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.
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.