Attention is All You Need is a research paper published by Google in 2017, introducing the concept of Transformers, a new model which significantly outperformed the existing models, revolutionizing how we understand and generate language.
What is Attention?
Imagine that you’re writing a sentence and you want to make sure it flows smoothly. The attention mechanism is like your editor, highlighting the important words as you go. It helps your AI model figure out which words in the sentence have more importance and which ones are less important. This allows the AI to focus on the right words at the right time, making the whole sentence sound natural and meaningful. This clever mechanism has improved how AI understands and creates language. Essentially, attention tells the model the relative importance of words in a sequence. Attention has a theoretically infinite reference window and allows for parallel computation, which GPUs are highly suited for in the current day.
Transformer Architecture
The architecture of Transformers contains 5 crucial parts. It begins with input embedding, a step that converts words or tokens into numerical vectors, enabling the model to work with them. Next comes the encoder, which is responsible for processing input sequences and adding contextual information. One of the most pivotal aspects of Transformers is the Query, Key, and Value (QKV) output representation, which empowers context-aware processing. The decoder, on the other hand, takes this context-rich information and generates output by attending to the encoded sequences, producing sequential predictions. Finally, we arrive at the output, which is the result of the Transformer model’s intricate computations. This architecture’s modular design, with its input embedding, encoder, QKV output, decoder, and output stages, has revolutionized AI applications by allowing for efficient parallel processing, making Transformers a necessity in natural language processing, translation, text generation, and beyond.
