  Generating natural and meaningful responses to communicate with multi-modal
human inputs is a fundamental capability of Large Vision-Language
Models(LVLMs). While current open-source LVLMs demonstrate promising
performance in simplified scenarios such as single-turn single-image input,
they fall short in real-world conversation scenarios such as following
instructions in a long context history with multi-turn and multi-images.
Existing LVLM benchmarks primarily focus on single-choice questions or
short-form responses, which do not adequately assess the capabilities of LVLMs
in real-world human-AI interaction applications. Therefore, we introduce MMDU,
a comprehensive benchmark, and MMDU-45k, a large-scale instruction tuning
dataset, designed to evaluate and improve LVLMs' abilities in multi-turn and
multi-image conversations. We employ the clustering algorithm to ffnd the
relevant images and textual descriptions from the open-source Wikipedia and
construct the question-answer pairs by human annotators with the assistance of
the GPT-4o model. MMDU has a maximum of 18k image+text tokens, 20 images, and
27 turns, which is at least 5x longer than previous benchmarks and poses
challenges to current LVLMs. Our in-depth analysis of 15 representative LVLMs
using MMDU reveals that open-source LVLMs lag behind closed-source counterparts
due to limited conversational instruction tuning data. We demonstrate that
ffne-tuning open-source LVLMs on MMDU-45k signiffcantly address this gap,
generating longer and more accurate conversations, and improving scores on MMDU
and existing benchmarks (MMStar: +1.1%, MathVista: +1.5%, ChartQA:+1.2%). Our
contributions pave the way for bridging the gap between current LVLM models and
real-world application demands. This project is available at
https://github.com/Liuziyu77/MMDU.
