Mitigating Hallucinations in Large Vision-Language Models without Performance Degradation

ArXi:2604.20366v1 Announce Type: new Large Vision-Language Models (LVLMs) exhibit powerful generative capabilities but frequently produce hallucinations that compromise output reliability. Fine-tuning on annotated data devoid of hallucinations offers the most direct solution, while its high computational cost motivates recent representation-based methods, which focus on mitigating hallucinatory components within hidden representations.