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差分Transformer:降噪耳机,让模型更专注

Transformer架构在自然语言处理领域取得了巨大成功,但其也存在一些缺陷。其中一个关键问题是 注意力噪声:模型会过度关注不相关的上下文信息,影响模型的性能。为了解决这一问题,微软研究院和清华大学的研究团队提出了一种新的 Transformer 架构:差分Transformer(Differential Transformer,简称 Diff Transformer)

Diff Transformer的核心创新在于引入了差分注意力机制(differential attention mechanism)。该机制类似于电气工程中的降噪耳机和差分放大器,通过利用两个 softmax 注意力函数之间的差来消除注意力噪声,从而鼓励模型重点关注关键信息。

差分注意力机制的工作原理如下:

  1. 将输入序列映射成查询、键和值向量。
  2. 使用查询和键向量计算注意力分数。
  3. 使用两个 softmax 函数计算注意力分数的差值,从而消除噪声。
  4. 将差值与值向量进行加权求和,得到最终的输出。

Diff Transformer 的优势:

  • 消除注意力噪声: 提升模型对关键信息的关注度,降低无关信息的影响。
  • 增强上下文建模能力: 提高模型对长序列文本的理解能力。
  • 简单易行: 在保持 Transformer 架构整体布局不变的情况下,仅用差分注意力替换传统 softmax 注意力。

实验结果表明,Diff Transformer 在多个 NLP 任务上取得了显著的性能提升。

Diff Transformer 的未来展望:

  • 进一步优化差分注意力机制: 例如探索更有效的噪声消除方法。
  • 将 Diff Transformer 应用于其他领域: 例如图像识别、语音识别等。

Diff Transformer 的出现为 Transformer 架构的改进提供了新的思路,也为 NLP 领域的发展带来了新的机遇。

参考文献:

  • Ye, Tianzhu, et al. Differential Transformer. arXiv preprint arXiv:2410.05258 (2024).

注: 本文参考了机器之心的报道以及论文原文,并进行了整理和补充。


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