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}.

翻译：集合标记（Set-of-Mark, SoM）提示方法通过将视觉对象与图像上插入的标签相关联，释放了GPT-4V的视觉定位能力。这些以字母数字标记的标签可通过文本标记进行索引，便于引用。尽管GPT-4V表现出卓越性能，但我们观察到其他多模态大语言模型（MLLMs）难以理解这些视觉标签。为促进开源模型对SoM提示的学习，我们提出一种新学习范式："逐项列举"，即要求模型按照标签的字母数字顺序枚举并描述图像上所有视觉标签。通过将我们整理的标注数据集与其他视觉指令调优数据集相结合，能够赋予现有MLLMs的SoM提示能力。进一步地，我们在五个MLLM基准上评估了微调后的SoM模型。研究发现，即使该数据集规模相对较小（1万至3万张带标签图像），也能显著增强MLLMs的视觉推理能力并减少幻觉现象。令人意外的是，这些改进在推理阶段移除图像输入中的视觉标签后仍然存在。这表明"逐项列举"作为MLLMs新训练范式的潜力，通过训练阶段使用视觉标签增强了物体-文本对齐。最后，我们通过探测训练模型进行机理分析，以理解SoM的工作机制。我们的代码与数据可通过\url{https://github.com/zzxslp/SoM-LLaVA}获取。