According to DeepLearning.AI, researchers have introduced Sample-Efficient Modality Integration (SEMI), a framework that enables any pretrained encoder—covering images, audio, video, sensors, and graphs—to be integrated into a large language model (LLM) using a single projector and LoRA adapters trained on just a handful of paired examples. SEMI, as detailed in The Batch, demonstrates strong few-shot adaptation across new domains by leveraging data-rich pretraining and then outperforming existing baselines on various multimodal tasks. This approach dramatically reduces the need for massive labeled datasets, unlocking practical business opportunities for rapid deployment of multimodal LLMs across industries with scarce annotation resources (source: deeplearning.ai/the-batch/semi-sample-efficient-modality-integration-tackles-new-domains-with-few-shot-examples/).