Papers by Ruei-Yao Sun

1 papers
Multi-CLS BERT: An Efficient Alternative to Traditional Ensembling (2023.acl-long)

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Challenge: ensembling BERT models often improves accuracy but at the cost of significantly more computation and memory footprint.
Approach: They propose a new ensembling method for CLS-based prediction tasks that is almost as efficient as a single BERT model.
Outcome: The proposed method outperforms existing BERT models on GLUE and SuperGLUE with 100 training samples.

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