2 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.CL2024
Mitigating Hallucination in Multimodal Large Language Model via Hallucination-targeted Direct Preference Optimization
Yuhan Fu, Ruobing Xie, Xingwu Sun +2
Multimodal Large Language Models (MLLMs) are known to hallucinate, which limits their practical applications. Recent works have attempted to apply Direct Preference Optimization (D…
cs.CL2024★ 2 cited
HMoE: Heterogeneous Mixture of Experts for Language Modeling
An Wang, Xingwu Sun, Ruobing Xie +9
Mixture of Experts (MoE) offers remarkable performance and computational efficiency by selectively activating subsets of model parameters. Traditionally, MoE models use homogeneous…
cs.CL2024
Truth Forest: Toward Multi-Scale Truthfulness in Large Language Models through Intervention without Tuning
Zhongzhi Chen, Xingwu Sun, Xianfeng Jiao +4
Despite the great success of large language models (LLMs) in various tasks, they suffer from generating hallucinations. We introduce Truth Forest, a method that enhances truthfulne…