Feb. 15, 2024, 5:42 a.m. | Yongchao Zhou, Uri Alon, Xinyun Chen, Xuezhi Wang, Rishabh Agarwal, Denny Zhou

cs.LG updates on arXiv.org arxiv.org

arXiv:2402.09371v1 Announce Type: new
Abstract: Length generalization, defined as the ability to extrapolate from shorter training sequences to longer test ones, is a significant challenge for language models. This issue persists even with large-scale Transformers handling relatively straightforward tasks. In this paper, we test the Transformer's ability of length generalization using the task of addition of two integers. We show that the success of length generalization is intricately linked to the data format and the type of position encoding. Using …

abstract arxiv challenge cs.ai cs.cl cs.lg issue language language models paper scale tasks test training transformer transformers type

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