English(EN) Type Diversity Enables Transformers to Generalise Compositionally

研究发现数据集多样性是 Transformer 组合泛化的关键

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-14 04:00

研究人员提出，Transformer 在组合任务中面临的结构泛化困难并非固有缺陷，而是源于数据集的局限性。通过增加数据集中结构类型的多样性，类似于现有词汇类型的多样性，Transformer 表现出更强的组合泛化能力。这一发现挑战了先前认为复合发散是导致这些困难的主要原因的论断，并表明数据集属性显著影响模型性能。 AI

影响提出了改进 Transformer 泛化能力的方法，可能影响未来的模型开发和评估。

排序理由学术论文发表在 arXiv 上，详细介绍了关于 Transformer 模型能力的新假设和实验结果。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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arXiv cs.CL TIER_1 English(EN) · Anssi Moisio, Mathias Creutz, Mikko Kurimo · 2026-09-14 04:00

类型多样性使Transformer能够进行组合泛化

arXiv:2609.13144v1 Announce Type: new Abstract: Compositional generalisation has been divided into lexical and structural generalisation. Previous work has found that structural generalisation is harder than lexical for Transformers. We propose that this difference is not inheren…

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类型多样性使Transformer能够进行组合泛化

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