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 …

Multi-modal large language models have demonstrated impressive performances on most
vision-language tasks. However, the model generally lacks the understanding capabilities …

Chart parsing poses a significant challenge due to the diversity of styles, values, texts, and
so forth. Even advanced large vision-language models (LVLMs) with billions of parameters …

Data visualization in the form of charts plays a pivotal role in data analysis, offering critical
insights and aiding in informed decision-making. Automatic chart understanding has …

Although most current large multimodal models (LMMs) can already understand photos of
natural scenes and portraits, their understanding of abstract images, eg, charts, maps, or …

Document parsing is essential for converting unstructured and semi-structured documents-
such as contracts, academic papers, and invoices-into structured, machine-readable data …

The rapid development of large language models (LLMs) has been witnessed in recent
years. Based on the powerful LLMs, multi-modal LLMs (MLLMs) extend the modality from …

Recently, many versatile Multi-modal Large Language Models (MLLMs) have emerged
continuously. However, their capacity to query information depicted in visual charts and …

Formula recognition presents significant challenges due to the complicated structure and
varied notation of mathematical expressions. Despite continuous advancements in formula …

Whilst fact verification has attracted substantial interest in the natural language processing
community, verifying misinforming statements against data visualizations such as charts has …