#MLLMs

GRIT introduces a groundbreaking method for teaching multimodal large language models to jointly reason with images and text, significantly improving visual grounding and reasoning accuracy while requiring minimal training data.

UniME introduces a two-stage framework that significantly improves multimodal representation learning by leveraging textual knowledge distillation and hard negative instruction tuning, outperforming existing models on multiple benchmarks.