标题： BoostTransformer：通过子网格选择和重要性采样增强Transformer模型 显示英文标题

标题： BoostTransformer: Enhancing Transformer Models with Subgrid Selection and Importance Sampling

摘要： Transformer架构在现代自然语言处理中占主导地位，但通常需要大量的计算资源和复杂的超参数调整。 为了缓解这些挑战，我们提出了一种新的框架BoostTransformer，该框架通过子网格标记选择和重要性加权采样将提升原则引入Transformer。 我们的方法直接将最小二乘提升目标整合到Transformer流程中，实现了更高效的训练和更好的性能。 在多个细粒度文本分类基准测试中，BoostTransformer表现出更快的收敛速度和更高的准确性，超越了标准Transformer，同时最小化了架构搜索的开销。 显示英文摘要

摘要： Transformer architectures dominate modern NLP but often demand heavy computational resources and intricate hyperparameter tuning. To mitigate these challenges, we propose a novel framework, BoostTransformer, that augments transformers with boosting principles through subgrid token selection and importance-weighted sampling. Our method incorporates a least square boosting objective directly into the transformer pipeline, enabling more efficient training and improved performance. Across multiple fine-grained text classification benchmarks, BoostTransformer demonstrates both faster convergence and higher accuracy, surpassing standard transformers while minimizing architectural search overhead.