MLLM Art Appreciation Evaluation Results and Correct Response Terms Appendix

Multi-modal large language models (MLLMs) are primarily evaluated on objective measures such as reasoning, common sense and pattern recognition. However, there is a notable lack of testing involving open-ended responses which require human evaluation. In response to this, this paper presents a comparative analysis of the capacities of GPT-4V, Gemini Pro, Gemini Ultra and MPLUG Owl2 in visual art appreciation, a domain requiring complex competencies demonstrative of higher order cognitive fluency thus presenting a ripe area for the evaluation of human-like intelligences.

A framework for the machine appreciation art was developed based on an established model of human aesthetic experience as a foundation. Seven questions were designed to assess each stage of this framework which outlines the nuanced capacities by which MLLMs can appreciate a visual art image. MLLMs were assessed on their long-form responses to this question set for ten distinct art images representing varying styles and mediums.