Transformer Architecture Simplified

Transformer architecture was introduced by a team of researchers at Google’s Brain division in a 2017 paper titled “Attention Is All You Need.” This advent of the transformer marked a seismic shift in the landscape of NLP. It ushered in the era of large language models (LLMs) that have since demonstrated remarkable advancements in the realm of NLP, surpassing the capabilities of earlier-generation recurrent neural networks (RNNs). From machine translation and sentiment analysis to question answering and text summarization, LLMs based on transformers have set new benchmarks and opened up exciting possibilities for the future of AI-driven language tasks.

Before we delve into the intricate details of the transformer architecture, let’s draw an analogy that simplifies the understanding of how transformers operate.

Imagine you are trying to comprehend a lengthy story. You can’t simply read the story from beginning to end, as you will forget what happened earlier in the narrative. Instead, you need to be able to focus on the important parts of the story and ignore the unimportant ones. Transformers function in a similar way. They can concentrate on the crucial elements of a sequence and disregard the less significant ones, even if the important parts are not consecutive.