MapIQ：评估多模态大语言模型的地图问答能力
MapIQ: Evaluating Multimodal Large Language Models for Map Question Answering
AI总结：
针对现有Map-VQA研究仅聚焦分级统计图、覆盖任务与主题有限的问题，提出含14706个问答对、覆盖三类地图六大主题的MapIQ基准，评估多款MLLMs的地图问答性能并探究地图设计变更对模型的影响。
摘要：
多模态大语言模型（MLLMs）的最新进展促使研究者探索这些模型读取条形图、散点图等数据可视化内容的能力。近期，研究关注点转向了地图视觉问答（Map-VQA），但现有Map-VQA研究主要聚焦于分级统计图（choropleth maps），仅覆盖有限的主题类别和视觉分析任务。为弥补这些不足，我们提出MapIQ基准数据集，包含14706个问答对，覆盖分级统计图、 cartograms（ Cartogram，即 cartogram 变形地图）、比例符号地图三类地图，涉及住房、犯罪等六大不同主题。我们通过六项视觉分析任务评估多款MLLMs，对比它们之间的性能及与人类基线的差距。额外实验探究了配色方案调整、图例设计修改、地图元素移除等地图设计变更的影响，揭示了MLLMs的鲁棒性与敏感性、其对内部地理知识的依赖，以及提升Map-VQA性能的潜在方向。
英文摘要：
Recent advancements in multimodal large language models (MLLMs) have driven researchers to explore how well these models read data visualizations, e.g., bar charts, scatter plots. More recently, attention has shifted to visual question answering with maps (Map-VQA). However, Map-VQA research has primarily focused on choropleth maps, which cover only a limited range of thematic categories and visual analytical tasks. To address these gaps, we introduce MapIQ, a benchmark dataset comprising 14,706 question-answer pairs across three map types: choropleth maps, cartograms, and proportional symbol maps spanning topics from six distinct themes (e.g., housing, crime). We evaluate multiple MLLMs using six visual analytical tasks, comparing their performance against one another and a human baseline. An additional experiment examining the impact of map design changes (e.g., altered color schemes, modified legend designs, and removal of map elements) provides insights into the robustness and sensitivity of MLLMs, their reliance on internal geographic knowledge, and potential avenues for improving Map-VQA performance.
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