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English(EN) Why Attend to Everything? Focus is the Key

Focus方法在不损失性能的情况下提高LLM注意力效率

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为Focus的新方法，旨在提高大型语言模型中注意力机制的效率。标准的注意力机制会随着序列长度呈二次方增长，导致计算成本高昂。Focus引入了少量可学习的质心作为门控，只允许相关的token对进行长距离的相互关注。该方法在各种模型规模的下游任务和困惑度基准测试中，均显示出能保持甚至提高性能，同时显著提高处理速度。 AI

影响 该方法可以显著降低LLM的计算成本，从而实现对更长序列的更快处理。

排序理由 这是一篇介绍语言模型注意力机制新方法的论文。

在 arXiv cs.CL 阅读 →

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Focus方法在不损失性能的情况下提高LLM注意力效率

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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Hengshuai Yao, Xing Chen, Ahmed Murtadha, Jin Li, Yasin Abbasi Yadkori, Shuai Shao, Changling Liu, Guan Wang, Mingli Yuan, William Chen, Sen Song ·

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    arXiv:2604.03260v2 Announce Type: replace Abstract: Standard attention scales quadratically with sequence length. Efficient attention methods reduce this O(n^2) cost, but when retrofitted into pretrained models, they often degrade perplexity, downstream accuracy, or both. We intr…

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