Differential Transformer
Authors: name: Tianzhu Ye, name: Li Dong, name: Yuqing Xia, name: Yutao Sun, name: Yi Zhu, name: Gao Huang, name: Furu Wei

Transformer tends to overallocate attention to irrelevant context. In this
work, we introduce Diff Transformer, which amplifies attention to the relevant
context while canceling noise. Specifically, the differential attention
mechanism calculates attention scores as the difference between two separate
softmax attention maps. The subtraction cancels noise, promoting the emergence
of sparse attention patterns. Experimental results on language modeling show
that Diff Transformer outperforms Transformer in various settings of scaling up
model size and training tokens. More intriguingly, it offers notable advantages
in practical applications, such as long-context modeling, key information
retrieval, hallucination mitigation, in-context learning, and reduction of
activation outliers. By being less distracted by irrelevant context, Diff
Transformer can mitigate hallucination in question answering and text
summarization. For in-context learning, Diff Transformer not only enhances
accuracy but is also more robust to order permutation, which was considered as
a chronic robustness issue. The results position Diff Transformer as a highly
effective and promising architecture to advance large language models.