563 citations · 813 across the 25 of their papers we have counts for
5 papers · 1 filter
Beyond Preserved Accuracy: Evaluating Loyalty and Robustness of BERT Compression
Canwen Xu, Wangchunshu Zhou, Tao Ge +3
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, lab…
Multitask Prompted Training Enables Zero-Shot Task Generalization
Victor Sanh, Albert Webson, Colin Raffel +38
Large language models have recently been shown to attain reasonable zero-shot generalization on a diverse set of tasks (Brown et al., 2020). It has been hypothesized that this is a…
Datasets: A Community Library for Natural Language Processing
Quentin Lhoest, Albert Villanova del Moral, Yacine Jernite +29
The scale, variety, and quantity of publicly-available NLP datasets has grown rapidly as researchers propose new tasks, larger models, and novel benchmarks. Datasets is a community…
BERT Learns to Teach: Knowledge Distillation with Meta Learning
Wangchunshu Zhou, Canwen Xu, Julian McAuley
We present Knowledge Distillation with Meta Learning (MetaDistil), a simple yet effective alternative to traditional knowledge distillation (KD) methods where the teacher model is…
Blow the Dog Whistle: A Chinese Dataset for Cant Understanding with Common Sense and World Knowledge
Canwen Xu, Wangchunshu Zhou, Tao Ge +3
Cant is important for understanding advertising, comedies and dog-whistle politics. However, computational research on cant is hindered by a lack of available datasets. In this pap…