Web: http://arxiv.org/abs/2110.07867

May 13, 2022, 1:11 a.m. | Yujia Qin, Xiaozhi Wang, Yusheng Su, Yankai Lin, Ning Ding, Jing Yi, Weize Chen, Zhiyuan Liu, Juanzi Li, Lei Hou, Peng Li, Maosong Sun, Jie Zhou

cs.CL updates on arXiv.org arxiv.org

Why can pre-trained language models (PLMs) learn universal representations
and effectively adapt to broad NLP tasks differing a lot superficially? In this
work, we empirically find evidence indicating that the adaptations of PLMs to
various few-shot tasks can be reparameterized as optimizing only a few free
parameters in a unified low-dimensional intrinsic task subspace, which may help
us understand why PLMs could easily adapt to various NLP tasks with small-scale
data. To find such a subspace and examine its universality, …


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