Are Differential Transformers Cut Through the Noise better than traditional ones ?

In recent years, transformers have taken the world of artificial intelligence (AI) by storm, becoming the backbone of powerful models like GPT and BERT. These models have shown incredible capabilities in understanding language, generating content, and even engaging in conversations with users. But as impressive as they are, transformers have a critical limitation — when they deal with large amounts of information, they struggle to separate the signal from the noise.

This is where Differential Transformers step in, offering a new approach to overcome these challenges.

Let’s break down this innovation in simple terms and use a story to explain how it’s different from traditional transformers.

The Problem with Traditional Transformers

Imagine you are a detective in a bustling city, trying to solve a mystery. Your job is to sift through thousands of clues to find the one piece of information that cracks the case. Now, picture yourself walking into a noisy marketplace, with hundreds of people shouting all at once. You need to focus on the crucial conversation that holds the key to solving the mystery, but the noise is overwhelming. This is what happens to traditional transformers — they perform exceptionally…