257 citations · 369 across the 28 of their papers we have counts for
5 papers · 1 filter
Go-tuning: Improving Zero-shot Learning Abilities of Smaller Language Models
Jingjing Xu, Qingxiu Dong, Hongyi Liu +1
With increasing scale, large language models demonstrate both quantitative improvement and new qualitative capabilities, especially as zero-shot learners, like GPT-3. However, thes…
Statistical Dataset Evaluation: Reliability, Difficulty, and Validity
Chengwen Wang, Qingxiu Dong, Xiaochen Wang +2
Datasets serve as crucial training resources and model performance trackers. However, existing datasets have exposed a plethora of problems, inducing biased models and unreliable e…
Calibrating Factual Knowledge in Pretrained Language Models
Qingxiu Dong, Damai Dai, Yifan Song +3
Previous literature has proved that Pretrained Language Models (PLMs) can store factual knowledge. However, we find that facts stored in the PLMs are not always correct. It motivat…
Neural Knowledge Bank for Pretrained Transformers
Damai Dai, Wenbin Jiang, Qingxiu Dong +3
The ability of pretrained Transformers to remember factual knowledge is essential but still limited for existing models. Inspired by existing work that regards Feed-Forward Network…
Robust Fine-tuning via Perturbation and Interpolation from In-batch Instances
Shoujie Tong, Qingxiu Dong, Damai Dai +4
Fine-tuning pretrained language models (PLMs) on downstream tasks has become common practice in natural language processing. However, most of the PLMs are vulnerable, e.g., they ar…