Self-Attention

Self-attention, also known as scaled dot-product attention, is a fundamental mechanism used in deep learning and natural language processing, particularly in transformer-based models like BERT, GPT, and their variants. Self-attention is a crucial component that enables these models to understand relationships and dependencies between words or tokens in a sequence.

Here's an overview of self-attention:

1. The Motivation:

   • The primary motivation behind self-attention is to capture dependencies and relationships between different elements within a sequence, such as words in a sentence or tokens in a document.

   • It allows the model to consider the context of each element based on its relationships with other elements in the sequence.

2. The Mechanism:

   • Self-attention computes a weighted sum of the input elements (usually vectors) for each element in the sequence. This means that each element can attend to and be influenced by all other elements.

   • The key idea is to learn weights (attention scores) that reflect how much focus each element should give to the others. These weights are often referred to as "attention weights."

3. Attention Weights:

   • Attention weights are calculated using a similarity measure (typically the dot product) between a query vector and a set of key vectors.

   • The resulting attention weights are then used to take a weighted sum of the value vectors. This weighted sum forms the output for each element.

4. Scaling and Softmax:

   • To stabilize the gradients during training, the dot products are often scaled by the square root of the dimension of the key vectors.

   • After scaling, a softmax function is applied to obtain the attention weights. The softmax ensures that the weights are normalized and sum to 1.

5. Multi-Head Attention:

   • Many models use multi-head attention, where multiple sets of queries, keys, and values are learned. Each set of attention weights captures different aspects of relationships in the sequence.

   • These multiple sets of attention results are concatenated and linearly transformed to obtain the final output.

6. Applications:

   • Self-attention is widely used in transformer-based models for various NLP tasks, including machine translation, text classification, text generation, and more.

   • It is also applied in computer vision tasks, such as image captioning, where it can capture relationships between different parts of an image.

Self-attention is a powerful mechanism because it allows the model to focus on different elements of the input sequence depending on the context. This enables the model to capture long-range dependencies, word relationships, and nuances in natural language, making it a crucial innovation in deep learning for NLP and related fields.