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 …

Causal inference has shown potential in enhancing the predictive accuracy, fairness,
robustness, and explainability of Natural Language Processing (NLP) models by capturing …

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 …

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 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 …

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 …

Hallucinations is a big shadow hanging over the rapidly evolving multimodal large language
models (MLLMs), referring to that the generated text is inconsistent with the image content …

We present Unified-IO 2 a multimodal and multi-skill unified model capable of following
novel instructions. Unified-IO 2 can use text images audio and/or videos as input and can …