  Set-of-Mark (SoM) Prompting unleashes the visual grounding capability of
GPT-4V, by enabling the model to associate visual objects with tags inserted on
the image. These tags, marked with alphanumerics, can be indexed via text
tokens for easy reference. Despite the extraordinary performance from GPT-4V,
we observe that other Multimodal Large Language Models (MLLMs) struggle to
understand these visual tags. To promote the learning of SoM prompting for
open-source models, we propose a new learning paradigm: "list items one by
one," which asks the model to enumerate and describe all visual tags placed on
the image following the alphanumeric orders of tags. By integrating our curated
dataset with other visual instruction tuning datasets, we are able to equip
existing MLLMs with the SoM prompting ability. Furthermore, we evaluate our
finetuned SoM models on five MLLM benchmarks. We find that this new dataset,
even in a relatively small size (10k-30k images with tags), significantly
enhances visual reasoning capabilities and reduces hallucinations for MLLMs.
Perhaps surprisingly, these improvements persist even when the visual tags are
omitted from input images during inference. This suggests the potential of
"list items one by one" as a new paradigm for training MLLMs, which strengthens
the object-text alignment through the use of visual tags in the training stage.
Finally, we conduct analyses by probing trained models to understand the
working mechanism of SoM. Our code and data are available at
\url{https://github.com/zzxslp/SoM-LLaVA}.
