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English(EN) LiFT: Local Search via Linear Programming for Overfitting-Controlled Transformers

新的LiFT框架使用线性规划来控制Transformer过拟合

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员推出了一种新颖的Transformer模型微调框架LiFT，该框架利用线性规划来控制过拟合。该方法将微调表述为一个双层优化问题，联合更新模型参数和正则化超参数。通过求解线性规划，LiFT识别出一种面向验证的下降方向以进行集中更新，从而减少了广泛重新训练的需求。在WikiText-2上对GPT-2 Small进行的实验表明，LiFT能够有效地调整Transformer块和正则化参数，尤其是在易于过拟合的情况下，提高了测试困惑度。 AI

影响 引入了一种原则性的Transformer微调方法，可以减轻过拟合，从而可能提高模型性能和泛化能力。

排序理由 该集群描述了一篇新的研究论文，其中详细介绍了一种新颖的Transformer模型微调方法。

在 arXiv cs.CL 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新的LiFT框架使用线性规划来控制Transformer过拟合

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该集群描述了一篇新的研究论文，其中详细介绍了一种新颖的Transformer模型微调方法。
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报道来源 [2]

  1. arXiv cs.CL TIER_1 English(EN) · Abhishek Shukla, Anikeit Khanna, Ankur Sinha, Faiz Hamid ·

    LiFT：通过线性规划进行局部搜索以控制过拟合的Transformer

    arXiv:2606.16243v1 Announce Type: cross Abstract: This paper proposes a Linear Programming (LP)-based local search framework for fine-tuning pretrained transformer models with explicit control against overfitting. The approach formulates transformer fine-tuning as a bilevel optim…

  2. arXiv cs.CL TIER_1 English(EN) · Faiz Hamid ·

    LiFT: 通过线性规划进行局部搜索以控制Transformer过拟合

    This paper proposes a Linear Programming (LP)-based local search framework for fine-tuning pretrained transformer models with explicit control against overfitting. The approach formulates transformer fine-tuning as a bilevel optimization-based regularization problem, in which mod…

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