According to @DeepLearningAI, the Sample-Efficient Modality Integration (SEMI) framework plugs any pretrained encoder for images, audio, video, sensors, or graphs into an LLM using a single projector plus LoRA adapters generated from a handful of paired examples, enabling multimodal LLMs without massive labeled datasets (source: DeepLearning.AI The Batch). Trained on data-rich domains, SEMI few-shot adapts to new domains and outperforms baselines across tasks, demonstrating strong sample efficiency for multimodal integration (source: DeepLearning.AI The Batch). For crypto and quant teams, the actionable takeaway is reduced labeled-data and ramp-up requirements for deploying multimodal analytics pipelines, though the source does not provide cost metrics or market performance links (source: DeepLearning.AI The Batch).

According to DeepLearning.AI on Twitter, the new 'Pretraining LLMs' course developed with UpstageAI highlights that while prompting or fine-tuning large language models (LLMs) is generally effective for broad language tasks, pretraining is crucial when targeting specialized domains or underrepresented languages. For AI-driven trading, this approach can enhance model accuracy in financial text analysis or crypto market sentiment when mainstream models fall short, offering a competitive edge for traders operating in niche or emerging markets (source: DeepLearning.AI, May 2, 2025).