Chart-to-Experience: Benchmarking Multimodal LLMs for Predicting Experiential Impact of Charts
This addresses the need for validated benchmarks in MLLM applications for chart analysis, though it is incremental as it focuses on a specific domain.
The paper tackled the problem of predicting the perceptual and emotional impact of charts using Multimodal Large Language Models (MLLMs), finding that MLLMs are less sensitive than humans in direct predictions but accurate in pairwise comparisons.
The field of Multimodal Large Language Models (MLLMs) has made remarkable progress in visual understanding tasks, presenting a vast opportunity to predict the perceptual and emotional impact of charts. However, it also raises concerns, as many applications of LLMs are based on overgeneralized assumptions from a few examples, lacking sufficient validation of their performance and effectiveness. We introduce Chart-to-Experience, a benchmark dataset comprising 36 charts, evaluated by crowdsourced workers for their impact on seven experiential factors. Using the dataset as ground truth, we evaluated capabilities of state-of-the-art MLLMs on two tasks: direct prediction and pairwise comparison of charts. Our findings imply that MLLMs are not as sensitive as human evaluators when assessing individual charts, but are accurate and reliable in pairwise comparisons.