How Transformers Revolutionized Large Language Models: A Story of Attention and Efficiency

Author(s): Souradip Pal

Originally published on Towards AI.

This member-only story is on us. Upgrade to access all of Medium.

The world of artificial intelligence (AI) has made remarkable strides in recent years, particularly in understanding human language. At the heart of this revolution is the Transformer model, a core innovation that allows large language models (LLMs) to process and understand language with an efficiency that previous models could only dream of. But how do Transformers work? To explain this, let’s take a journey through their inner workings, using stories and analogies to make the complex concepts easier to grasp.

Imagine reading a book, but only through a small keyhole. You can only see one word at a time, and while you’re aware of the words that came before, it’s difficult to piece everything together. This is exactly the challenge that traditional models, like Recurrent Neural Networks (RNNs) or Long Short-Term Memory networks (LSTMs), faced. They processed sequences word by word, which made it hard to keep track of long-term dependencies.

For example, these models might struggle to connect the meaning of a word at the beginning of a sentence with a word at the end. It’s like trying to recall details from a story when you’ve only… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI