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English(EN) SHIFT-LLM: Distribution Shift Correction in Depth-Pruned LLMs

SHIFT-LLM 框架校正大模型深度剪枝，显著恢复准确率

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了 SHIFT-LLM，一个新颖的框架，旨在校正经过深度剪枝的大语言模型中发生的准确率损失。该方法在剪枝位置插入轻量级线性残差适配器 (LRAs)，在无需梯度计算的情况下近似移除模块的隐藏状态。SHIFT-LLM 已证明能够显著恢复准确率，在 Llama 3.1 8B-Instruct 上准确率提升高达 15.7 个百分点，仅使用了几百个校准样本。 AI

影响 这项研究提供了一种通过剪枝层来降低大模型推理成本的方法，同时减轻了准确率损失，有望实现更高效的大模型部署。

排序理由 详细介绍大模型优化新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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SHIFT-LLM 框架校正大模型深度剪枝，显著恢复准确率

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详细介绍大模型优化新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Ali Bahri, Hang Li, Hongliang Li, Zhitang Chen ·

    SHIFT-LLM：深度剪枝大模型中的分布偏移校正

    arXiv:2608.25068v1 Announce Type: new Abstract: Depth pruning removes entire Transformer blocks to reduce the inference cost of large language models, but disrupts the hidden-state distributions expected by downstream layers, leading to significant accuracy loss. We introduce SHI…

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