5 citations · 9 across the 3 of their papers we have counts for
10 papers · 1 filter
Watermarking Conditional Text Generation for AI Detection: Unveiling Challenges and a Semantic-Aware Watermark Remedy
Yu Fu, Deyi Xiong, Yue Dong
To mitigate potential risks associated with language models, recent AI detection research proposes incorporating watermarks into machine-generated text through random vocabulary re…
Faithful to the Document or to the World? Mitigating Hallucinations via Entity-linked Knowledge in Abstractive Summarization
Yue Dong, John Wieting, Pat Verga
Despite recent advances in abstractive summarization, current summarization systems still suffer from content hallucinations where models generate text that is either irrelevant or…
On-the-Fly Attention Modulation for Neural Generation
Yue Dong, Chandra Bhagavatula, Ximing Lu +4
Despite considerable advancements with deep neural language models (LMs), neural text generation still suffers from degeneration: the generated text is repetitive, generic, self-co…
Multi-Fact Correction in Abstractive Text Summarization
Yue Dong, Shuohang Wang, Zhe Gan +3
Pre-trained neural abstractive summarization systems have dominated extractive strategies on news summarization performance, at least in terms of ROUGE. However, system-generated a…
Factual Error Correction for Abstractive Summarization Models
Meng Cao, Yue Dong, Jiapeng Wu +1
Neural abstractive summarization systems have achieved promising progress, thanks to the availability of large-scale datasets and models pre-trained with self-supervised methods. H…
Countering the Effects of Lead Bias in News Summarization via Multi-Stage Training and Auxiliary Losses
Matt Grenander, Yue Dong, Jackie Chi Kit Cheung +1
Sentence position is a strong feature for news summarization, since the lead often (but not always) summarizes the key points of the article. In this paper, we show that recent neu…