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Computer Science > Computation and Language

arXiv:2410.05258 (cs)
[Submitted on 7 Oct 2024]

Title:Differential Transformer

Authors:Tianzhu Ye, Li Dong, Yuqing Xia, Yutao Sun, Yi Zhu, Gao Huang
, Furu Wei
View a PDF of the paper titled Differential Transformer, by Tianzhu
Ye and 6 other authors
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    Abstract: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.

Subjects: Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as:  arXiv:2410.05258 [cs.CL]
          (or arXiv:2410.05258v1 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2410.05258
          Focus to learn more
          arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Li Dong [view email]
[v1] Mon, 7 Oct 2024 17:57:38 UTC (429 KB)
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