169 citations · 1.5k across the 75 of their papers we have counts for
25 papers · 1 filter
Pseudo-OOD training for robust language models
Dhanasekar Sundararaman, Nikhil Mehta, Lawrence Carin
While pre-trained large-scale deep models have garnered attention as an important topic for many downstream natural language processing (NLP) tasks, such models often make unreliab…
FairFil: Contrastive Neural Debiasing Method for Pretrained Text Encoders
Pengyu Cheng, Weituo Hao, Siyang Yuan +2
Pretrained text encoders, such as BERT, have been applied increasingly in various natural language processing (NLP) tasks, and have recently demonstrated significant performance ga…
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…
Improving Text Generation with Student-Forcing Optimal Transport
Guoyin Wang, Chunyuan Li, Jianqiao Li +10
Neural language models are often trained with maximum likelihood estimation (MLE), where the next word is generated conditioned on the ground-truth word tokens. During testing, how…
Graph Optimal Transport for Cross-Domain Alignment
Liqun Chen, Zhe Gan, Yu Cheng +3
Cross-domain alignment between two sets of entities (e.g., objects in an image, words in a sentence) is fundamental to both computer vision and natural language processing. Existin…