2 citations · 3 across the 4 of their papers we have counts for
5 papers · 1 filter
DHScore: Reasoning-Aware Hallucination Detection via Semantic Breadth and Depth Analysis in LLMs
Yue Ding, Xiaofang Zhu, Tianze Xia +4
Although large Language Models (LLMs) have achieved remarkable success, their practical application is often hindered by the generation of non-factual content, which is called "hal…
Mixture of Decoding: An Attention-Inspired Adaptive Decoding Strategy to Mitigate Hallucinations in Large Vision-Language Models
Xinlong Chen, Yuanxing Zhang, Qiang Liu +3
Large Vision-Language Models (LVLMs) have exhibited impressive capabilities across various visual tasks, yet they remain hindered by the persistent challenge of hallucinations. To…
A Survey on Personalized Alignment -- The Missing Piece for Large Language Models in Real-World Applications
Jian Guan, Junfei Wu, Jia-Nan Li +2
Large Language Models (LLMs) have demonstrated remarkable capabilities, yet their transition to real-world applications reveals a critical limitation: the inability to adapt to ind…
Adversarial Contrastive Learning for Evidence-aware Fake News Detection with Graph Neural Networks
Junfei Wu, Weizhi Xu, Qiang Liu +2
The prevalence and perniciousness of fake news have been a critical issue on the Internet, which stimulates the development of automatic fake news detection in turn. In this paper,…
Evidence-aware Fake News Detection with Graph Neural Networks
Weizhi Xu, Junfei Wu, Qiang Liu +2
The prevalence and perniciousness of fake news has been a critical issue on the Internet, which stimulates the development of automatic fake news detection in turn. In this paper,…