Beyond Preserved Accuracy: Evaluating Loyalty and Robustness of BERT Compression

Canwen Xu, Wangchunshu Zhou, Tao Ge, Ke Xu, Julian McAuley, Furu Wei
2021 Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing   unpublished
Recent studies on compression of pretrained language models (e.g., BERT) usually use preserved accuracy as the metric for evaluation. In this paper, we propose two new metrics, label loyalty and probability loyalty that measure how closely a compressed model (i.e., student) mimics the original model (i.e., teacher). We also explore the effect of compression with regard to robustness under adversarial attacks. We benchmark quantization, pruning, knowledge distillation and progressive module
more » ... cing with loyalty and robustness. By combining multiple compression techniques, we provide a practical strategy to achieve better accuracy, loyalty and robustness. 1
doi:10.18653/v1/2021.emnlp-main.832 fatcat:mxpovceylzdefbktsqxr6b53uu