  Large Language Models (LLMs) are capable of reasoning over diverse input data
modalities through pre-trained encoders. However, the growing diversity of
input data modalities prevents incorporating all modalities into LLMs,
especially when LLMs are deployed on resource-constrained edge devices for
embodied AI applications. Instead, a better option is to adaptively involve
only the useful modalities at runtime, depending on the current environmental
contexts and task requirements. For such modality adaptation, existing work
adopts fixed connections between encoders and the LLM's input layer, leading to
high training cost at runtime and ineffective cross-modal interaction. In this
paper, we address these limitations by presenting mPnP-LLM, a new technique
that allows fully elastic, automated and prompt runtime modality adaptation, by
connecting unimodal encoders to a flexible set of last LLM blocks and making
such latent connections fully trainable at runtime. Experiments over the
nuScenes-QA dataset show that mPnP-LLM can achieve up to 3.7x FLOPs reduction
and 30% GPU memory usage reduction, while retaining on-par accuracy with the
existing schemes. Under the same compute budget, mPnP-LLM improves the task
accuracy by up to 4% compared to the best existing scheme.
