Beyond Preserved Accuracy: Evaluating Loyalty and Robustness of BERT Compression
Canwen Xu, Wangchunshu Zhou, Tao Ge, Ke Xu, Julian McAuley, Furu Wei
Abstract
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 replacing with loyalty and robustness. By combining multiple compression techniques, we provide a practical strategy to achieve better accuracy, loyalty and robustness.- Anthology ID:
- 2021.emnlp-main.832
- Volume:
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2021
- Address:
- Online and Punta Cana, Dominican Republic
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 10653–10659
- Language:
- URL:
- https://aclanthology.org/2021.emnlp-main.832
- DOI:
- 10.18653/v1/2021.emnlp-main.832
- Cite (ACL):
- Canwen Xu, Wangchunshu Zhou, Tao Ge, Ke Xu, Julian McAuley, and Furu Wei. 2021. Beyond Preserved Accuracy: Evaluating Loyalty and Robustness of BERT Compression. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 10653–10659, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Beyond Preserved Accuracy: Evaluating Loyalty and Robustness of BERT Compression (Xu et al., EMNLP 2021)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2021.emnlp-main.832.pdf
- Code
- jetrunner/beyond-preserved-accuracy
- Data
- GLUE, MultiNLI