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 …

This survey addresses the crucial issue of factuality in Large Language Models (LLMs). As
LLMs find applications across diverse domains, the reliability and accuracy of their outputs …

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 …

Efficient fine-tuning is vital for adapting large language models (LLMs) to downstream tasks.
However, it requires non-trivial efforts to implement these methods on different models. We …

AI legal assistants based on Large Language Models (LLMs) can provide accessible legal
consulting services, but the hallucination problem poses potential legal risks. This paper …

The unprecedented advancements in Large Language Models (LLMs) have shown a
profound impact on natural language processing but are yet to fully embrace the realm of 3D …

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

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 …