1 citations · 1 across the 3 of their papers we have counts for
5 papers
Predicting Multi-View Rashomon Representation: Can We Learn Where Models Disagree?
Mingyue Ma, Zongbo Han, Changqing Zhang +1
Foundation models are increasingly adopted across a wide range of applications, often serving as core blocks within AI systems. Yet different foundation models may encode the same…
Fusion Anything: A Generalized Multimodal Foundation Model
Huizi Cui, Zongbo Han, Chenggong Ding +6
Making prediction with multimodal data is widely used in diverse scenarios. Existing multimodal fusion models, once deployed, can only handle predefined modalities (e.g., vision, t…
Semantic Energy: Detecting LLM Hallucination Beyond Entropy
Huan Ma, Jiadong Pan, Jing Liu +7
Large Language Models (LLMs) are being increasingly deployed in real-world applications, but they remain susceptible to hallucinations, which produce fluent yet incorrect responses…
Helping CLIP See Both the Forest and the Trees: A Decomposition and Description Approach
Leyan Xue, Zongbo Han, Guangyu Wang +3
Vision-Language Models (VLMs) like CLIP achieve cross-modal semantic alignment through contrastive learning, exhibiting robust zero-shot generalization. Traditional prompt engineer…
Estimating LLM Uncertainty with Evidence
Huan Ma, Jingdong Chen, Joey Tianyi Zhou +2
Over the past few years, Large Language Models (LLMs) have developed rapidly and are widely applied in various domains. However, LLMs face the issue of hallucinations, generating r…