In this work, we discuss building performant Multimodal Large Language Models (MLLMs).
In particular, we study the importance of various architecture components and data choices …

The field of efficient Large Language Model (LLM) inference is rapidly evolving, presenting a
unique blend of opportunities and challenges. Although the field has expanded and is …

Large vision-language models (LVLMs) have recently achieved rapid progress, sparking
numerous studies to evaluate their multi-modal capabilities. However, we dig into current …

We present MM1. 5, a new family of multimodal large language models (MLLMs) designed
to enhance capabilities in text-rich image understanding, visual referring and grounding …

We introduce LLaVA-MoD, a novel framework designed to enable the efficient training of
small-scale Multimodal Language Models (s-MLLM) by distilling knowledge from large-scale …

In recent years, multimodal large language models (MLLMs) have made significant strides
by training on vast high-quality image-text datasets, enabling them to generally understand …

In this work, we introduce DOPRA, a novel approach designed to mitigate hallucinations in
multi-modal large language models (MLLMs). Unlike existing solutions that typically involve …

We present the ShareGPT4Video series, aiming to facilitate the video understanding of large
video-language models (LVLMs) and the video generation of text-to-video models (T2VMs) …

Charts are important for presenting and explaining complex data relationships. Recently,
multimodal large language models (MLLMs) have shown remarkable capabilities in various …

Current multimodal Large Language Models (MLLMs) suffer from``hallucination'',
occasionally generating responses that are not grounded in the input images. To tackle this …