A human-annotated benchmark with 4,800 QA pairs evaluates how well AI models can retrieve and generate interleaved text-and-image answers.
WikiWeb2M: A Page-Level Multimodal Wikipedia Dataset
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abstract
Webpages have been a rich resource for language and vision-language tasks. Yet only pieces of webpages are kept: image-caption pairs, long text articles, or raw HTML, never all in one place. Webpage tasks have resultingly received little attention and structured image-text data underused. To study multimodal webpage understanding, we introduce the Wikipedia Webpage 2M (WikiWeb2M) suite; the first to retain the full set of images, text, and structure data available in a page. WikiWeb2M can be used for tasks like page description generation, section summarization, and contextual image captioning.
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MRAMG-Bench: A Comprehensive Benchmark for Advancing Multimodal Retrieval-Augmented Multimodal Generation
A human-annotated benchmark with 4,800 QA pairs evaluates how well AI models can retrieve and generate interleaved text-and-image answers.