MLLMs generate verbose, comprehensive, and repetitive aesthetic critiques unlike selective human ones, and reference-based metrics fail to detect this because they capture model house style instead of image-specific content.
Training-free mitiga- tion of adversarial attacks on deep learning-based mri recon- struction.arXiv preprint arXiv:2501.01908, 2025
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Can MLLMs Critique Like Humans? Evaluating Open-Ended Aesthetic Reasoning in Multimodal Large Language Models
MLLMs generate verbose, comprehensive, and repetitive aesthetic critiques unlike selective human ones, and reference-based metrics fail to detect this because they capture model house style instead of image-specific content.