@inproceedings{ye-etal-2026-kidsartbench,
    title = "{K}ids{A}rt{B}ench: Multi-Dimensional Children{'}s Art Evaluation with Attribute-Aware {MLLM}s",
    author = "Ye, Mingrui  and
      Zheng, Chanjin  and
      Yu, Zengyi  and
      Xiang, Chenyu  and
      Zhao, Zhixue  and
      Yuan, Zheng  and
      Yannakoudakis, Helen",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.267/",
    pages = "5702--5722",
    ISBN = "979-8-89176-380-7",
    abstract = "Multimodal Large Language Models (MLLMs) show progress across many visual{--}language tasks; however, their capacity to evaluate artistic expression remains limited: aesthetic concepts are inherently abstract and open-ended, and multimodal artwork annotations are scarce. We introduce KidsArtBench, a new benchmark of over 1k children{'}s artworks (ages 5-15) annotated by 12 expert educators across 9 rubric-aligned dimensions, together with expert comments for feedback. Unlike prior aesthetic datasets that provide single scalar scores on adult imagery, KidsArtBench targets children{'}s artwork and pairs multi-dimensional annotations with comment supervision to enable both ordinal assessment and formative feedback. Building on this resource, we propose an attribute-specific multi-LoRA approach {--} where each attribute corresponds to a distinct evaluation dimension (e.g., Realism, Imagination) in the scoring rubric {--} with Regression-Aware Fine-Tuning (RAFT) to align predictions with ordinal scales. On Qwen2.5-VL-7B, our method increases correlation from 0.468 to 0.653, with the largest gains on perceptual dimensions and narrowed gaps on higher-order attributes. Our results show that educator-aligned supervision and attribute-aware training yield pedagogically meaningful evaluations and establish a rigorous testbed for sustained progress in educational AI. We release data and code with ethics documentation."
}