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VLMs reveal hidden temporal insights in art history, but their biases could mislead interpretations of historical timelines. WHY_IT MATTERS: This research challenges the reliability of pretrained models in historical contexts, highlighting the need for critical evaluation of data representation in AI systems.
XAI methods for VLMs struggle to align with human interpretation in art history, revealing limitations in capturing nuanced, context-dependent reasoning.
Unlock art history insights with FRAME, a meticulously annotated dataset that lets you train LLMs to understand the intricate relationships within artworks.
