We propose SPHINX-X, an extensive Multimodality Large Language Model (MLLM) series
developed upon SPHINX. To improve the architecture and training efficiency, we modify the …

We present SPHINX, a versatile multi-modal large language model (MLLM) with a joint
mixing of model weights, tuning tasks, and visual embeddings. First, for stronger vision …

In the era of advanced multimodel learning, multimodal large language models (MLLMs)
such as GPT-4V have made remarkable strides towards bridging language and visual …

Although instruction-tuned large language models (LLMs) have exhibited remarkable
capabilities across various NLP tasks, their effectiveness on other data modalities beyond …

Large language models (LLMs) have demonstrated remarkable language abilities. GPT-4,
based on advanced LLMs, exhibits extraordinary multimodal capabilities beyond previous …

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

Multimodal Large Language Models (MLLMs) have demonstrated notable capabilities in
general visual understanding and reasoning tasks. However, their deployment is hindered …

While recently Multimodal Large Language Models (MM-LLMs) have made exciting strides,
they mostly fall prey to the limitation of only input-side multimodal understanding, without the …

We introduce Blink, a new benchmark for multimodal language models (LLMs) that focuses
on core visual perception abilities not found in other evaluations. Most of the Blink tasks can …

The rapid evolution of Multi-modality Large Language Models (MLLMs) has catalyzed a shift
in computer vision from specialized models to general-purpose foundation models …