3 papers
cs.LG2025
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…
cs.CV2025
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…
cs.CL2025
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…