159 citations · 910 across the 32 of their papers we have counts for
33 papers
HiddenCut: Simple Data Augmentation for Natural Language Understanding with Better Generalization
Jiaao Chen, Dinghan Shen, Weizhu Chen +1
Fine-tuning large pre-trained models with task-specific data has achieved great success in NLP. However, it has been demonstrated that the majority of information within the self-a…
What Makes Good In-Context Examples for GPT-?
Jiachang Liu, Dinghan Shen, Yizhe Zhang +3
GPT- has attracted lots of attention due to its superior performance across a wide range of NLP tasks, especially with its powerful and versatile in-context few-shot learning ab…
MixKD: Towards Efficient Distillation of Large-scale Language Models
Kevin J Liang, Weituo Hao, Dinghan Shen +4
Large-scale language models have recently demonstrated impressive empirical performance. Nevertheless, the improved results are attained at the price of bigger models, more power c…
A Simple but Tough-to-Beat Data Augmentation Approach for Natural Language Understanding and Generation
Dinghan Shen, Mingzhi Zheng, Yelong Shen +2
Adversarial training has been shown effective at endowing the learned representations with stronger generalization ability. However, it typically requires expensive computation to…
CoDA: Contrast-enhanced and Diversity-promoting Data Augmentation for Natural Language Understanding
Yanru Qu, Dinghan Shen, Yelong Shen +3
Data augmentation has been demonstrated as an effective strategy for improving model generalization and data efficiency. However, due to the discrete nature of natural language, de…
Improving Self-supervised Pre-training via a Fully-Explored Masked Language Model
Mingzhi Zheng, Dinghan Shen, Yelong Shen +2
Masked Language Model (MLM) framework has been widely adopted for self-supervised language pre-training. In this paper, we argue that randomly sampled masks in MLM would lead to un…