做 VQA 或视觉语言模型评测的团队终于有了一个真正需要外部知识的基准，不再是纯视觉感知题——想测试模型知识推理能力的可以直接用这个数据集。
WikiVQABench 是一个人工策划的知识驱动视觉问答（VQA）基准，通过系统结合 Wikipedia 图像、文章标题和 Wikidata 结构化知识构建。它使用大语言模型生成候选多项选择题，再由人工审核确保事实正确性和视觉-文本一致性，要求每个问题必须依赖外部知识才能正确回答。评估了 15 个视觉语言模型（256M-90B 参数），准确率范围从 24.7% 到 75.6%，表明该基准能有效区分模型在知识密集型推理上的能力。数据集和代码已公开。
WikiVQABench: A Knowledge-Grounded Visual Question Answering Benchmark from Wikipedia and Wikidata
Visual Question Answering (VQA) benchmarks have largely emphasized perception-based tasks that can be solved from visual content alone. In contrast, many real-world scenarios require external knowledge that is not directly observable in the image to answer correctly. We introduce WikiVQABench, a human-curated knowledge-grounded VQA benchmark constructed by systematically combining Wikipedia images, their associated article captions, and structured knowledge from Wikidata. Our pipeline uses large language models (LLMs) to generate candidate multiple-choice image-question-answer sets. All generated instances are subsequently reviewed and curated by human annotators to ensure factual correctness, visual-text consistency, and that each question requires external knowledge in addition to visual evidence for correct resolution. WikiVQABench comprises a substantial collection of Wikipedia images with curated multiple-choice questions designed to benchmark knowledge-aware vision-language models (VLMs). Evaluation of fifteen VLMs (256M-90B parameters) reveals a wide performance range (24.7%-75.6% accuracy), demonstrating that the benchmark effectively discriminates model capabilities on knowledge-intensive reasoning. The dataset and benchmarking code are publicly available.