English(EN) Evaluating Compositional Generalization in Transformers: The Role of Composition Equivalence and Module Coverage

新研究因捷径学习质疑 Transformer 的泛化能力声明

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

一篇新的 arXiv 论文研究了 Transformer 模型中的组合泛化能力，发现由于忽略了组合等价性和模块覆盖率等因素，之前的评估一直不一致。研究表明，模型经常利用组合等价性等捷径，导致对其真实泛化能力的估计过高。当移除这些捷径时，性能会显著下降，这凸显了设计更鲁棒的基准测试以考虑这些因素的必要性。 AI

影响强调了 Transformer 泛化能力可能被高估，敦促改进基准测试设计。

排序理由该集群包含一篇详细介绍 Transformer 模型研究结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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Newsworthiness bucket: Tool; 该集群包含一篇详细介绍 Transformer 模型研究结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.CL TIER_1 English(EN) · Purva Pruthi, Andrew Yuan, Alexander D'Amour, David Jensen · 2026-11-11 05:00

评估 Transformer 中的组合泛化：组合等价性和模块覆盖率的作用

arXiv:2611.13498v1 Announce Type: cross Abstract: Compositional generalization is an important form of out-of-distribution generalization, defined as the ability to train on some combinations of modules (e.g., functions such as sort, filter) and then generalize to unseen combinat…

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报道来源 [1]

评估 Transformer 中的组合泛化：组合等价性和模块覆盖率的作用

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