10 citations · 12 across the 3 of their papers we have counts for
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cs.CL2022
Beyond Prompting: Making Pre-trained Language Models Better Zero-shot Learners by Clustering Representations
Yu Fei, Ping Nie, Zhao Meng +2
Recent work has demonstrated that pre-trained language models (PLMs) are zero-shot learners. However, most existing zero-shot methods involve heavy human engineering or complicated…
cs.CL2020★ 2 cited
A Pairwise Probe for Understanding BERT Fine-Tuning on Machine Reading Comprehension
Jie Cai, Zhengzhou Zhu, Ping Nie +1
Pre-trained models have brought significant improvements to many NLP tasks and have been extensively analyzed. But little is known about the effect of fine-tuning on specific tasks…
cs.CL2020★ 10 cited
DC-BERT: Decoupling Question and Document for Efficient Contextual Encoding
Yuyu Zhang, Ping Nie, Xiubo Geng +3
Recent studies on open-domain question answering have achieved prominent performance improvement using pre-trained language models such as BERT. State-of-the-art approaches typical…