Qinglin He

Modality-Fair Preference Optimization for Trustworthy MLLM Alignment

Multimodal large language models (MLLMs) have achieved remarkable success across various tasks. However, separate training of visual and textual encoders often results in a misalignment of the modality. Such misalignment may lead models to generate content that is absent from the input image, a phen