English(EN) Rethinking Data Selection as Distribution Matching on Semantic Manifolds

新的QMAC方法优化LLM训练数据选择

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-09 05:00

研究人员推出了一种名为QMAC的新方法，用于选择高质量数据来训练大型语言模型和视觉-语言模型。QMAC通过将数据选择构建为分布匹配和质量过滤的联合优化问题，解决了海量数据集相关的计算和存储成本问题。该方法确保所选子集的分布在嵌入空间中与完整数据集对齐，同时过滤掉噪声或低质量样本。在不同数据类型和模型上的实验表明，QMAC的性能优于现有方法，在某些场景下甚至超越了完整数据训练。 AI

影响优化训练数据选择，可能降低成本并提高模型性能。

排序理由这是一篇研究论文，详细介绍了LLM训练中数据选择的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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报道来源 [1]

arXiv cs.CL TIER_1 English(EN) · Peng Sun, Yi Yang, Mingshuo Chen, Chunxiao Li, Yao Zhu, Huawen Shen, Tianfan Fu · 2026-11-09 05:00

将数据选择视为语义流形上的分布匹配

arXiv:2611.08495v1 Announce Type: cross Abstract: Instruction tuning of large language models and large vision language models relies on massive training data, which incurs substantial computational and storage costs. Meanwhile, training data often contains redundancy, noise, and…

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报道来源 [1]

将数据选择视为语义流形上的分布匹配

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