Transformers Architecture: One-Stop Detailed Guide: Part 3— Self Attention
The self-attention mechanism is a cornerstone of transformer architectures, powering revolutionary advancements in AI, particularly in generative AI. By dynamically adjusting word representations based on their context, self-attention enables models to understand the relationships between words in a sequence. This blog explores self-attention in depth, starting from its motivation to its mathematical foundations and refinements with learnable parameters.
Why Self-Attention Matters
The rise of transformer architectures has fundamentally transformed the landscape of NLP and generative AI. At the heart of this transformation lies self-attention, which addresses critical limitations of earlier methods by creating contextual embeddings — word representations that vary depending on the surrounding words in a sentence.
Self-attention’s ability to capture context is indispensable for tasks like machine translation, text summarization, and question answering. Let’s understand how it works.
From Word Embeddings to Contextual Embeddings
The Static Nature of Word Embeddings
Word embeddings provide a numerical representation of words, enabling NLP models to process textual data. However, traditional embeddings are static, meaning they remain the same regardless of the context in which the word appears.
Example:
"bank"in"money bank"refers to a financial institution."bank"in"river bank"refers to the edge of a river.
A static embedding for "bank" cannot differentiate between these contexts, leading to inaccuracies in tasks requiring contextual understanding.
Building Self-Attention from Scratch
To address the limitations of static embeddings, self-attention generates contextual embeddings — dynamic representations of words influenced by their surrounding words in a sentence.
The Concept
In self-attention, a word’s representation is influenced by all the other words in the sentence. For example:
- In
"Money bank grows", the contextual embedding for"bank"incorporates information from"money","bank", and"grows". - Similarly, in
"River bank flows", the contextual embedding for"bank"incorporates information from"river","bank", and"flows".
Each word is represented as a weighted combination of all the words in the sentence, where the weights represent the relevance or similarity between the words.
Mathematical Representation
Self-attention generates new word embeddings using the following process:
Calculating Similarities (Weights)
Steps in Self-Attention
- Compute Similarities
Calculate the dot product between the embedding of the target word and the embeddings of all words in the sentence. - Normalize Similarities
Use the softmax function to convert the similarities into probabilities, ensuring the weights sum to 1. - Compute Weighted Sum
Multiply the normalized weights with the embeddings of the corresponding words and sum them to generate the new contextual embedding. - Repeat for All Words
Perform the same process for every word in the sentence to compute their contextual embeddings.
Enhancing Self-Attention with Learnable Parameters
While the basic self-attention mechanism provides general contextual embeddings, it lacks learnable parameters, limiting its ability to adapt to specific NLP tasks. The introduction of learnable parameters refines the mechanism, enabling it to generate task-specific contextual embeddings.
Introducing Learnable Parameters
To incorporate learnable parameters, each word embedding is transformed into three specialized vectors:
- Query (Q): Represents the target word asking for relevance.
- Key (K): Represents the other words providing relevance information.
- Value (V): Represents the actual information used in the weighted sum.
Steps with Learnable Parameters
- Generate Query, Key, and Value Vectors
Transform each word embedding into query, key, and value vectors using the respective weight matrices. - Compute Similarities
Calculate the similarity between the query vector of the target word and the key vectors of all words. - Normalize Similarities
Apply the softmax function to generate normalized weights. - Compute Weighted Sum
Use the normalized weights to compute a weighted sum of the value vectors. This weighted sum becomes the new contextual embedding for the word.
Advantages of Learnable Parameters
- Task-Specific Contextual Embeddings
The model learns to tailor embeddings to the specific NLP task, improving performance. - Scalability and Efficiency
Despite the added complexity, self-attention remains computationally efficient due to its parallel processing capability.
Advantages and Disadvantages of Self-Attention
Advantages
- Contextual Understanding
Dynamically adjusts word representations based on context, addressing the limitations of static embeddings. - Parallel Computation
Allows for efficient processing of all words simultaneously, leveraging modern hardware like GPUs. - Scalability
Handles long sequences effectively, making it suitable for large datasets.
Disadvantages
- Loss of Sequential Order
Self-attention does not explicitly account for word order, which can be crucial for understanding text in some tasks. - High Computational Cost
Calculating similarities and performing matrix operations require significant computational resources, especially for large datasets.
Conclusion
Self-attention is the backbone of transformer architectures, enabling the creation of contextual embeddings that dynamically adapt to the surrounding words in a sentence. By introducing learnable parameters, self-attention becomes even more powerful, allowing models to specialize for specific NLP tasks while maintaining computational efficiency.
This exploration provides a solid foundation for understanding the inner workings of transformers. In the next blog, we’ll explore scaled dot product attention. Stay tuned!
This blog is part of a series unraveling the mysteries of transformers, breaking down their complexities into digestible insights. Follow along as we continue to dive deeper into this revolutionary AI architecture!
