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  • title: A Win-win Deal: Towards Sparse and Robust Pre-trained Language Models
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            A Win-win Deal: Towards Sparse and Robust Pre-trained Language Models
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            A Win-win Deal: Towards Sparse and Robust Pre-trained Language Models

            Dec 6, 2022

            Speakers

            YL

            Yuanxin Liu

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            FM

            Fandong Meng

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            ZL

            Zheng Lin

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            About

            Despite the remarkable success of pre-trained language models (PLMs), they still face two challenges: First, large-scale PLMs are inefficient in terms of memory footprint and computation. Second, on the downstream tasks, PLMs tend to rely on the dataset bias and struggle to generalize to out-of-distribution (OOD) data. In response to the efficiency problem, recent studies show that dense PLMs can be replaced with sparse subnetworks without hurting the performance. Such subnetworks can be found i…

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