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English(EN) Efficient Data Selection for Multimodal Models via Incremental Optimization Utility

新框架高效选择多模态模型数据

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一个名为 One-Step-Train (OST) 的新框架，用于高效选择高质量的合成数据来训练大型多模态模型 (LMM)。OST 将数据选择重构为一个增量优化效用问题，通过在代理模型上进行模拟的单步更新来估计样本效用。与 LLM-as-a-Judge 等方法相比，这种方法显著降低了训练成本和时间，同时还提高了在基准测试上的性能并缓解了噪声数据的问题。 AI

影响 该方法可以显著降低训练大型多模态模型的计算成本，使其更易于访问和更高效。

排序理由 该集群描述了一篇关于为特定人工智能研究问题提出新颖框架和方法论的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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新框架高效选择多模态模型数据

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该集群描述了一篇关于为特定人工智能研究问题提出新颖框架和方法论的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Zhan Su ·

    通过增量优化效用实现多模态模型的有效数据选择

    The scaling of Large Multimodal Models (LMMs) is constrained by the quality-quantity trade-off inherent in synthetic data. Previous approaches, such as LLM-as-a-Judge, have proven their effectiveness in addressing this but suffer from prohibitive computational costs and lack of i…

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