This paper embarks on an exploration into the Large Language Model (LLM) datasets,
which play a crucial role in the remarkable advancements of LLMs. The datasets serve as …

Large multimodal models (LMMs) have shown great results in single-image vision language
tasks. However, their abilities to solve multi-image visual language tasks is yet to be …

Multimodal Large Language Models (MLLMs) have gained significant attention recently,
showing remarkable potential in artificial general intelligence. However, assessing the utility …

While large language models (LLMs) excel in a simulated world of texts they struggle to
interact with the more realistic world without perceptions of other modalities such as visual or …

In the complex and multidimensional field of medicine, multimodal data are prevalent and
crucial for informed clinical decisions. Multimodal data span a broad spectrum of data types …

We introduce InternVL 2.5, an advanced multimodal large language model (MLLM) series
that builds upon InternVL 2.0, maintaining its core model architecture while introducing …

While large vision-language models (LVLMs) have demonstrated impressive capabilities in
interpreting multi-modal contexts, they invariably suffer from object hallucinations (OH). We …

Current benchmarks for assessing vision-language models (VLMs) often focus on their
perception or problem-solving capabilities and neglect other critical aspects such as …

As a prominent direction of Artificial General Intelligence (AGI), Multimodal Large Language
Models (MLLMs) have garnered increased attention from both industry and academia …

Abstract Recent Large Vision Language Models (LVLMs) present remarkable zero-shot
conversational and reasoning capabilities given multimodal queries. Nevertheless, they …