Sangyun Chung

MAD: Modality-Adaptive Decoding for Mitigating Cross-Modal Hallucinations in Multimodal Large Language Models

This work proposes Modality-Adaptive Decoding (MAD), a training-free method that adaptively weights modality-specific decoding branches based on task requirements based on task requirements, demonstrating that explicit modality awareness through self-assessment is crucial for robust multimodal reasoning.