Large Language Models (LLMs) have recently transformed both the academic and industrial
landscapes due to their remarkable capacity to understand, analyze, and generate texts …

With the widespread application of the Transformer architecture in various modalities,
including vision, the technology of large language models is evolving from a single modality …

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 …

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

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 …

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 …

We introduce CogVLM, a powerful open-source visual language foundation model. Different
from the popular shallow alignment method which maps image features into the input space …

The remarkable progress of Multi-modal Large Language Models (MLLMs) has gained
unparalleled attention. However, their capabilities in visual math problem-solving remain …

Visual encoding constitutes the basis of large multimodal models (LMMs) in understanding
the visual world. Conventional LMMs process images in fixed sizes and limited resolutions …

Visual instruction tuning has made considerable strides in enhancing the capabilities of
Large Multimodal Models (LMMs). However, existing open LMMs largely focus on single …