Multimodal Large Language Models (LLMs) extend the capabilities of text-based language models by incorporating additional input modalities such as images, audio, video, and structured data. These models are trained on paired cross-modal datasets to develop shared representations that support tasks including visual reasoning, document analysis, and multimodal dialogue. Research directions include improving modality alignment, scaling cross-modal pretraining, reducing hallucination, and adapting multimodal LLMs to specialized scientific and enterprise domains.
Machine learning is shifting from learning from data alone to learning from both data and teacher models. Beta-KD uses uncertainty-aware Bayesian weighting to train compact multimodal AI without blindly trusting every teacher signal.
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