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 …

With the deepening of research on Large Language Models (LLMs), significant progress has
been made in recent years on the development of Large Multimodal Models (LMMs), which …

We introduce MMMU: a new benchmark designed to evaluate multimodal models on
massive multi-discipline tasks demanding college-level subject knowledge and deliberate …

Large vision-language models (VLMs) have recently achieved remarkable progress,
exhibiting impressive multimodal perception and reasoning abilities. However, effectively …

In this paper, we introduce InternVL 1.5, an open-source multimodal large language model
(MLLM) to bridge the capability gap between open-source and proprietary commercial …

Abstract The Large Vision-Language Model (LVLM) field has seen significant
advancements, yet its progression has been hindered by challenges in comprehending fine …

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

We present DeepSeek-VL, an open-source Vision-Language (VL) Model designed for real-
world vision and language understanding applications. Our approach is structured around …

In this work, we introduce Mini-Gemini, a simple and effective framework enhancing multi-
modality Vision Language Models (VLMs). Despite the advancements in VLMs facilitating …

Hallucination posed as a pervasive challenge of multi-modal large language models
(MLLMs) has significantly impeded their real-world usage that demands precise judgment …