Abstract Information Extraction (IE) aims to extract structural knowledge from plain natural
language texts. Recently, generative Large Language Models (LLMs) have demonstrated …

We introduce phi-3-mini, a 3.8 billion parameter language model trained on 3.3 trillion
tokens, whose overall performance, as measured by both academic benchmarks and …

We present LLaVA-OneVision, a family of open large multimodal models (LMMs) developed
by consolidating our insights into data, models, and visual representations in the LLaVA …

We present InternLM-XComposer-2.5 (IXC-2.5), a versatile large-vision language model that
supports long-contextual input and output. IXC-2.5 excels in various text-image …

Multi-modal Large Language Models have demonstrated remarkable capabilities in
executing instructions for a variety of single-image tasks. Despite this progress, significant …

The development of video large multimodal models (LMMs) has been hindered by the
difficulty of curating large amounts of high-quality raw data from the web. To address this, we …

With the burgeoning growth of online video platforms and the escalating volume of video
content, the demand for proficient video understanding tools has intensified markedly. Given …

Long-context capability is critical for multi-modal foundation models, especially for long
video understanding. We introduce LongVILA, a full-stack solution for long-context visual …

Rapid advancements have been made in extending Large Language Models (LLMs) to
Large Multi-modal Models (LMMs). However, extending input modality of LLMs to video data …

The remarkable multimodal capabilities and interactive experience of GPT-4o underscore
their necessity in practical applications, yet open-source models rarely excel in both areas …