  Large vision-language models (LVLMs) have recently witnessed rapid
advancements, exhibiting a remarkable capacity for perceiving, understanding,
and processing visual information by connecting visual receptor with large
language models (LLMs). However, current assessments mainly focus on
recognizing and reasoning abilities, lacking direct evaluation of
conversational skills and neglecting visual storytelling abilities. In this
paper, we propose an evaluation method that uses strong LLMs as judges to
comprehensively evaluate the various abilities of LVLMs. Firstly, we construct
a comprehensive visual dialogue dataset TouchStone, consisting of open-world
images and questions, covering five major categories of abilities and 27
subtasks. This dataset not only covers fundamental recognition and
comprehension but also extends to literary creation. Secondly, by integrating
detailed image annotations we effectively transform the multimodal input
content into a form understandable by LLMs. This enables us to employ advanced
LLMs for directly evaluating the quality of the multimodal dialogue without
requiring human intervention. Through validation, we demonstrate that powerful
LVLMs, such as GPT-4, can effectively score dialogue quality by leveraging
their textual capabilities alone, aligning with human preferences. We hope our
work can serve as a touchstone for LVLMs' evaluation and pave the way for
building stronger LVLMs. The evaluation code is available at
https://github.com/OFA-Sys/TouchStone.
