Large Language Models (LLMs) have recently demonstrated remarkable capabilities in
natural language processing tasks and beyond. This success of LLMs has led to a large …

Large models represent a groundbreaking advancement in multiple application fields,
enabling remarkable achievements across various tasks. However, their unprecedented …

Large vision-language models (VLMs) have recently achieved remarkable progress,
exhibiting impressive multimodal perception and reasoning abilities. However, effectively …

In this work, we introduce the Qwen-VL series, a set of large-scale vision-language models
(LVLMs) designed to perceive and understand both texts and images. Starting from the …

The Large Vision-Language Model (LVLM) has enhanced the performance of various
downstream tasks in visual-language understanding. Most existing approaches encode …

Large language models are now tuned to align with the goals of their creators, namely to be"
helpful and harmless." These models should respond helpfully to user questions, but refuse …

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 …

Large language models (LLMs) have achieved remarkable progress in solving various
natural language processing tasks due to emergent reasoning abilities. However, LLMs …

We introduce MMMU: a new benchmark designed to evaluate multimodal models on
massive multi-discipline tasks demanding college-level subject knowledge and deliberate …

In this work, we introduce the Qwen-VL series, a set of large-scale vision-language models
(LVLMs) designed to perceive and understand both texts and images. Starting from the …