36 citations · 136 across the 17 of their papers we have counts for
5 papers · 1 filter
RHO (): Reducing Hallucination in Open-domain Dialogues with Knowledge Grounding
Ziwei Ji, Zihan Liu, Nayeon Lee +4
Dialogue systems can leverage large pre-trained language models and knowledge to generate fluent and informative responses. However, these models are still prone to produce halluci…
Plausible May Not Be Faithful: Probing Object Hallucination in Vision-Language Pre-training
Wenliang Dai, Zihan Liu, Ziwei Ji +2
Large-scale vision-language pre-trained (VLP) models are prone to hallucinate non-existent visual objects when generating text based on visual information. In this paper, we system…
Effective Transfer Learning for Low-Resource Natural Language Understanding
Zihan Liu
Natural language understanding (NLU) is the task of semantic decoding of human languages by machines. NLU models rely heavily on large training data to ensure good performance. How…
Mere Contrastive Learning for Cross-Domain Sentiment Analysis
Yun Luo, Fang Guo, Zihan Liu +1
Cross-domain sentiment analysis aims to predict the sentiment of texts in the target domain using the model trained on the source domain to cope with the scarcity of labeled data.…
SNP2Vec: Scalable Self-Supervised Pre-Training for Genome-Wide Association Study
Samuel Cahyawijaya, Tiezheng Yu, Zihan Liu +4
Self-supervised pre-training methods have brought remarkable breakthroughs in the understanding of text, image, and speech. Recent developments in genomics has also adopted these p…