A Multimodal LLM (Large Language Model) is a neural network architecture that processes and generates content across multiple data modalities, such as text, images, audio, and video, within a unified model. By aligning representations from different input types, multimodal LLMs support tasks such as visual question answering, image captioning, document understanding, and cross-modal reasoning. Research challenges include modality alignment, efficient fusion of heterogeneous inputs, and generalization across diverse data types and domains.

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Uncertainty-Aware Knowledge Distillation for Multimodal Large Language Models

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Knowledge distillation establishes a learning paradigm that leverages both data supervision and teacher guidance. However, determining the optimal balance between learning from data and learning from the teacher is challenging, as some samples may be noisy while others are subject to teacher uncertainty. This motivates the need for adaptively balancing data and teacher supervision. We propose Beta-weighted Knowledge Distillation (Beta-KD), an uncertainty-aware distillation framework that adaptively modulates how much the student relies on teacher guidance. Specifically, we formulate teacher–student learning from a unified Bayesian perspective and interpret teacher supervision as a Gibbs prior over student activations. This yields a closed-form, uncertainty-aware weighting mechanism and supports arbitrary distillation objectives and their combinations. Extensive experiments on multimodal VQA benchmarks demonstrate that distilling student Vision-Language Models from a large teacher VLM consistently improves performance. The results show that Beta-KD outperforms existing knowledge distillation methods.

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