Differential Transformers

LLMs work better when they ignore unimportant info

Can we train Transformers to focus more on what's important and less on irrelevant details?

In this post, we'll explore a new architecture called the Differential Transformer. It's designed to enhance the attention mechanism in Transformers (“differential” here referring to subtraction, btw, not differential equations), helping models pay more attention to relevant information while reducing the influence of noise.

By the way, you can check out a short video summary of this paper and many others on the new Youtube channel!

Overview

Transformers have become a cornerstone in language modeling and natural language processing. They use an attention mechanism to weigh the importance of different parts of the input when making predictions. However, a common issue is that Transformers often allocate attention to irrelevant context, which can dilute their focus on essential information.

Related reading: Researchers discover explicit registeres eliminate vision transformer attention spikes.

“Figure 1: Transformer often over-attends to irrelevant context (i.e., attention noise). DIFF Transformer amplifies attention to answer spans and cancels noise, enhancing the capability of context modeling.”

The Differential Transformer (paper is here) introduces a novel attention mechanism aimed at addressing this problem. By modifying how attention scores are calculated, it amplifies attention to relevant context while canceling out noise. This approach has the potential to improve the model's ability to handle long sequences, retrieve key information, and reduce hallucinations in generated text.

Plain English Explanation

One way to think about this: Regular Transformers are like trying to listen to someone in a noisy cafe while all the background chatter competes for your attention. The Differential Transformer acts like noise-canceling headphones, helping you focus on the person speaking by subtracting the ambient sounds.