1. Attention mechanisms
In this video, we will dive into how the attention mechanism works, exploring its power to capture relationships between words and improve language modeling.
2. Attention mechanisms
Attention mechanisms help language models understand complex structures and represent text more effectively by focusing on important words and their relationships.
To better understand how attention works, consider reading a mystery book. As you would focus on clues while ignoring less important content, attention enables models to identify and concentrate on crucial input data.
3. Self-attention and multi-head attention
Now that we better understand attention as a concept let's explore its two primary types - self-attention and multi-head attention.
Self-attention weighs the importance of each word in a sentence based on the context to capture long-range dependencies. Multi-head attention takes self-attention to the next level by splitting the input into multiple "heads".
Each head focuses on different aspects of the relationships between words, allowing the model to learn a richer representation of the text.
4. Attention in a party
Let's look at an example, starting with attention and later extending it to differentiate between self and multi-head attention.
In a group conversation at a party, it is common to selectively pay attention to the most relevant speakers to understand the topic being discussed.
By filtering out background noise or less important comments, individuals can focus on the key points of the conversation and understand what is being discussed.
5. Party continues
Self-attention can be compared to focusing on each person's words in the group conversation and evaluating their relevance in relation to other people's words.
This technique enables the model to weigh each speaker's input and combine them to form a more comprehensive understanding of the conversation.
In contrast, multi-head attention involves splitting attention into multiple "channels" that simultaneously focus on different aspects of the conversation.
For instance, one channel may concentrate on the speakers' emotions, another on the primary topic, and a third on related side topics.
Each aspect is processed independently, and the resulting understandings are merged to gain a holistic perspective of the conversation.
6. Multi-head attention advantages
Let's review this using text.
Consider the following sentence: "The boy went to the store to buy some groceries, and he found a discount on his favorite cereal."
The model pays more attention to relevant words such as "boy", "store", "groceries", and "discount" to grasp the idea that the boy found a discount on groceries at the store.
When using self-attention, the model might weigh the connection between "boy" and "he" recognizing that they refer to the same person.
It also identifies the connection between "groceries" and "cereal" as related items within the store.
Multi-head attention is like having multiple self-attention mechanisms working simultaneously. It allows the model to split its focus into multiple channels where one channel might focus on the main character ("boy"), another on what he's doing ("went to the store," "found a discount"), and a third on the things involved ("groceries," "cereal").
These two attention mechanisms work together to give the model a comprehensive understanding of the sentence.
7. Let's practice!
Now that you understand the attention mechanism and its different types, it's time to put your knowledge into practice.