EduArt: An educational-level benchmark for evaluating art history knowledge in large language models
Large language models now score near ceiling on general benchmarks, but these aggregate measures reveal little about how models behave within single disciplines. Existing art-focused evaluations rely on synthetic questions and rarely report item-level properties. This paper introduces EduArt, an educational-level benchmark for art-historical knowledge and visual reasoning in multimodal LLMs. EduArt comprises 871 human-authored questions from Italian secondary-school exercises and US Advanced Placement Art History exams, spanning two languages and seven formats from multiple choice to in-text w
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 46%Knowledge Distillation of Black-Box Large Language Models →
- LinkedLinked via arxiv author · 85%Gianmarco Spinaci →
“EduArt: An educational-level benchmark for evaluating art history knowledge in large language models”
- LinkedLinked via arxiv author · 85%Lukas Klic →
“EduArt: An educational-level benchmark for evaluating art history knowledge in large language models”
- LinkedLinked via arxiv author · 85%Giovanni Colavizza →
“EduArt: An educational-level benchmark for evaluating art history knowledge in large language models”
- PossiblePossibly related (embedding) · 46%seehiong/eduvis →
- PossiblePossibly related (embedding) · 47%[Paper] How much do language models memorize? →
- FuzzySimilar title/name (fuzzy) · 59%jeinlee1991/chinese-llm-benchmark →
“Fuzzy title match (0.73): “EduArt: An educational-level benchmark for evaluating art hi” ≈ “jeinlee1991/chinese-llm-benchmark””
- PossiblePossibly related (embedding) · 49%EY re-envisions RAG around multimodal knowledge graphs to improve accuracy - SiliconANGLE →