EduArt: An educational-level benchmark for evaluating art history knowledge in large language models
arXiv:2607.02007v1 Announce Type: new Abstract: 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
As large language models achieve near-ceiling performance on general benchmarks, the need for discipline-specific evaluations like EduArt becomes critical to understand their true utility and limitations.
This benchmark provides a crucial tool for evaluating the nuanced capabilities of multimodal LLMs in specialized domains, moving beyond aggregate scores to assess practical knowledge application.
The focus shifts from general AI performance metrics to granular, domain-specific assessments, enabling more targeted development and application of LLMs in fields requiring deep expertise.
- · AI researchers
- · Educational technology sector
- · Specialized content creators
- · Cultural institutions
- · LLMs with broad but shallow knowledge
- · Generalist AI evaluation methods
EduArt will improve the fidelity of LLM evaluations in art history, revealing specific strengths and weaknesses.
This improved evaluation will drive the development of LLMs with deeper, more reliable domain-specific knowledge.
Specialized LLMs could fundamentally alter how research, education, and content creation are performed in fields like art history.
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Read at arXiv cs.CL