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English(EN) Lil: Less is Less When Applying Post-Training Sparse-Attention Algorithms in Long-Decode Stage

新方法应对大语言模型长上下文效率挑战 · 跟踪 3 个来源

作者 PulseAugur 编辑部 · [3 个来源] ·

研究人员正在开发新方法来提高大语言模型长上下文推理的效率。一种名为 LISA 的方法将线性注意力与稀疏注意力机制相结合，将计算复杂度从 O(n^2) 降低到 O(nM)，在某些模型上实现了 50% 的推理速度提升。另一种方法 ELSAA 使用稀疏和低秩分支来近似注意力分数，从而能够在不具体化完整注意力矩阵的情况下进行更长上下文的训练。第三篇论文 Lil 探讨了稀疏注意力可能由于序列更长而导致复杂性增加的“少即是少”问题，并提出了一种提前停止算法来缓解此问题。 AI

影响 这些进展旨在使大语言模型能够更有效地处理显著更长的上下文，从而可能提高复杂推理任务的性能，并更广泛地部署先进的 AI 功能。

排序理由 三篇研究论文提出了用于 Transformer 中高效注意力机制的新颖方法。

在 arXiv cs.AI 阅读 →

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新方法应对大语言模型长上下文效率挑战 · 跟踪 3 个来源

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三篇研究论文提出了用于 Transformer 中高效注意力机制的新颖方法。
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报道来源 [3]

  1. arXiv cs.AI TIER_1 English(EN) · Yu Zhao, Zekun Zhang, Fan Jiang, Bo Zeng, Linlong Xu, Shimin Shan, Yu Liu, Longyue Wang, Weihua Luo ·

    LISA: 用于高效长上下文推理的线性索引稀疏注意力

    arXiv:2607.19358v1 Announce Type: new Abstract: Recent advances in long chain-of-thought reasoning models such as DeepSeek-R1 have led to increasingly longer inference context lengths under the test-time scaling paradigm. However, the O(n^2) computational complexity of standard s…

  2. arXiv cs.AI TIER_1 English(EN) · Mahdi Heidari, Mohammad Mahdi Rahimi, Jaekyun Moon ·

    ELSAA：高效低秩稀疏注意力近似用于Transformer训练

    arXiv:2607.20214v1 Announce Type: cross Abstract: The quadratic $N\times N$ attention score matrix remains a central obstacle to extending Transformers to longer input lengths. Existing efficient attention methods usually reduce this bottleneck by either imposing sparsity, so tha…

  3. arXiv cs.AI TIER_1 English(EN) · Junhao Hu, Fangze Li, Mingtao Xu, Feifan Meng, Shiju Zhao, Tiancheng Hu, Ting Peng, Anmin Liu, Wenrui Huang, Chenxu Liu, Ziyue Hua, Tao Xie ·

    Lil：在长解码阶段应用训练后稀疏注意力算法时，少即是少

    arXiv:2601.03043v4 Announce Type: replace-cross Abstract: Large language models (LLMs) demonstrate strong capabilities across a wide range of complex tasks and are increasingly deployed at scale, placing significant demands on inference efficiency. Prior work typically decomposes…

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