# Differential Transformer

**Authors:** Tianzhu Ye, Li Dong, Yuqing Xia, Yutao Sun, Yi Zhu, Gao Huang, Furu Wei  
**arXiv:** [2410.05258](https://arxiv.org/abs/2410.05258)  
**Date:** 2024-10-07  
**Summary page:** https://www.summarizepaper.com/en/paper/2410.05258v1/

## Key points

- The Transformer model in natural language processing has been groundbreaking but tends to allocate excessive attention to irrelevant context, leading to inefficiencies.
- Researchers including Tianzhu Ye, Li Dong, Yuqing Xia, Yutao Sun, Yi Zhu, Gao Huang, and Furu Wei have introduced the Differential Transformer architecture to address this issue.
- The Differential Transformer enhances the model's ability to focus on relevant context while filtering out noise through a unique differential attention mechanism.
- Experimental results show that the Diff Transformer surpasses the traditional Transformer model across various scenarios involving scaling up model size and training tokens.
- It excels in practical applications such as long-context modeling and key information retrieval while addressing challenges like hallucination mitigation and in-context learning.
- Diff Transformer mitigates hallucination in question answering and text summarization tasks by reducing distractions from irrelevant context and showcases robustness against order permutation for in-context learning tasks.

## Summary

In the realm of natural language processing, the Transformer model has been a groundbreaking advancement. However, one of its limitations is its tendency to allocate excessive attention to irrelevant context, leading to inefficiencies in processing tasks. To address this issue, a team of researchers including Tianzhu Ye, Li Dong, Yuqing Xia, Yutao Sun, Yi Zhu, Gao Huang, and Furu Wei have introduced a novel architecture known as the Differential Transformer. The Differential Transformer enhances the model's ability to focus on relevant context while effectively filtering out noise. This is achieved through a unique differential attention mechanism that calculates attention scores by taking the difference between two distinct softmax attention maps. By subtracting these maps from each other, the model is able to cancel out irrelevant information and promote the emergence of sparse attention patterns. Experimental results conducted on language modeling tasks have demonstrated that the Diff Transformer surpasses its predecessor, the traditional Transformer model, across various scenarios involving scaling up model size and training tokens. Moreover, it exhibits significant advantages in practical applications such as long-context modeling and key information retrieval. It also addresses challenges like hallucination mitigation and in-context learning while reducing activation outliers. One particularly intriguing aspect of Diff Transformer is its ability to mitigate hallucination in question answering and text summarization tasks by reducing distractions caused by irrelevant context. Additionally, for in-context learning tasks, Diff Transformer not only enhances accuracy but also showcases robustness against order permutation – a longstanding challenge in ensuring model stability. Overall,the results obtained from this study position Diff Transformer as a highly effective and promising architecture for advancing large language models. The innovative approach taken by this research team offers valuable insights into improving attention mechanisms within transformer models and opens up new possibilities for enhancing performance across various NLP applications.