Abstract Recently, Multimodal Large Language Model (MLLM) represented by GPT-4V has
been a new rising research hotspot, which uses powerful Large Language Models (LLMs) …

In the past year, MultiModal Large Language Models (MM-LLMs) have undergone
substantial advancements, augmenting off-the-shelf LLMs to support MM inputs or outputs …

We propose MM-Vet, an evaluation benchmark that examines large multimodal models
(LMMs) on complicated multimodal tasks. Recent LMMs have shown various intriguing …

Large Vision-Language Models (LVLMs) have recently played a dominant role in
multimodal vision-language learning. Despite the great success, it lacks a holistic evaluation …

Computational pathology, has witnessed considerable progress in the development of both
task-specific predictive models and task-agnostic self-supervised vision encoders …

We propose InternLM-XComposer, a vision-language large model that enables advanced
image-text comprehension and composition. The innovative nature of our model is …

The exponential growth of large language models (LLMs) has opened up numerous
possibilities for multi-modal AGI systems. However the progress in vision and vision …

A primary hurdle of autonomous driving in urban environments is understanding complex
and long-tail scenarios, such as challenging road conditions and delicate human behaviors …

Finetuning large language models (LLMs) on instructions leads to vast performance
improvements on natural language tasks. We apply instruction tuning using code …

We introduce" HallusionBench" a comprehensive benchmark designed for the evaluation of
image-context reasoning. This benchmark presents significant challenges to advanced large …