English(EN) ResCom: Activation Residual Compensation for Large Language Model Quantization

新的ResCom技术在量化后提升大语言模型准确性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-28 04:00

研究人员开发了一种名为ResCom的新技术，以提高大语言模型量化后的准确性。该方法通过使用低秩校正来补偿层输出中的错误，从而解决了激活舍入造成的性能下降问题。ResCom在Llama-3-8B模型上展示了显著的改进，降低了困惑度，并提高了在WikiText-2、MMLU和GSM8K等基准测试上的准确性。该技术对各种模型尺寸都有效，并在内存减少和性能之间取得了平衡。 AI

影响通过提高量化后大语言模型的准确性，增强了部署大语言模型的效率。

排序理由该集群描述了一种在学术论文中提出的用于改进大语言模型量化效果的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

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Newsworthiness bucket: Tool; 该集群描述了一种在学术论文中提出的用于改进大语言模型量化效果的新方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Ning Yang, Bohan Cui, Zhuowen Liu, Yuting Wang · 2026-10-28 04:00

ResCom：大型语言模型量化的激活残差补偿

arXiv:2610.35955v1 Announce Type: cross Abstract: Low-bit weight--activation quantization reduces the memory and computation needed to deploy large language models, but activation rounding can still degrade accuracy after calibration. Reconstruction from rounded inputs cannot dis…

本文如何被排名

报道来源 [1]

ResCom：大型语言模型量化的激活残差补偿

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