标题： MapIQ：用于地图问答的多模态大语言模型基准测试 显示英文标题

标题： MapIQ: Benchmarking Multimodal Large Language Models for Map Question Answering

摘要： 多模态大语言模型（MLLMs）的最新进展促使研究人员探索这些模型在阅读数据可视化方面的能力，例如条形图、散点图。 最近，研究重点转向了地图上的视觉问答（Map-VQA）。 然而，Map-VQA的研究主要集中在等值线地图上，这仅涵盖了有限的主题类别和视觉分析任务。 为了解决这些差距，我们引入了MapIQ，这是一个基准数据集，包含三种地图类型（等值线地图、统计地图和比例符号地图）的14,706个问题-答案对，涵盖六个不同主题（例如，住房、犯罪）。 我们使用六个视觉分析任务对多个MLLMs进行了评估，比较了它们之间的性能以及与人类基线的性能。 一项额外的实验考察了地图设计变化（例如，颜色方案的改变、图例设计的修改以及地图元素的移除）的影响，提供了关于MLLMs的鲁棒性和敏感性的见解，以及它们对内部地理知识的依赖性，以及提高Map-VQA性能的潜在途径。 显示英文摘要

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.