IMPROVING MULTIMODAL MODELS IN AUTOMATED ARTWORK EVALUATION WITH OPTIMIZED SHOT SELECTION

Automated artwork evaluation with multimodal large language models (MLLMs) is useful for art education and analysis, but few-shot performance can be unre liable when the examples supplied in context are not representative. We study an optimized shot-selection strategy for improving MLLM-based artwork evalu ation. Specifically, we compare random selection with K-Means and K-Medoids clustering, selecting representative artwork examples as few-shot demonstrations. Experiments on ratings of 337 student artworks show that K-Medoids generally provides more stable correlations with expert ratings and is especially effective for Color Richness and Picture Organization. The results also show that the num ber of shots has dimension-specific effects: additional examples can help some dimensions while harming others. These findings indicate that representative, outlier-resistant shot selection is an important component of reliable automated artwork evaluation.