The deployment of large language models (LLMs) within the healthcare sector has sparked
both enthusiasm and apprehension. These models exhibit the remarkable ability to provide …

Multi-modal large language models (MLLMs) have made significant strides in various visual
understanding tasks. However, the majority of these models are constrained to process low …

The rapid development of large language and vision models (LLVMs) has been driven by
advances in visual instruction tuning. Recently, open-source LLVMs have curated high …

Most large multimodal models (LMMs) are implemented by feeding visual tokens as a
sequence into the first layer of a large language model (LLM). The resulting architecture is …

Large language models (LLMs) based on decoder-only transformers have demonstrated
superior text understanding capabilities compared to CLIP and T5-series models. However …

The ability to accurately interpret complex visual information is a crucial topic of multimodal
large language models (MLLMs). Recent work indicates that enhanced visual perception …

Large vision-language models (LVLMs) have shown promising performance on a variety of
vision-language tasks. However, they remain susceptible to hallucinations, generating …

Authors' Contact Information: Arpita Vats, arpita. vats09@ gmail. com, Santa Clara
University, Santa Clara, California, USA; Rahul Raja, Carnegie Mellon University …

Abstract The Mixture of Experts (MoE) architecture has evolved as a powerful and versatile
approach for improving the performance and efficiency of deep learning models. This survey …