Chart-to-Experience: Benchmarking Multimodal LLMs for Predicting Experiential Impact of Charts

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 assump...

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Bibliographic Details
Published in:IEEE Pacific Visualization Symposium pp. 340 - 345
Main Authors: Kim, Seon Gyeom, Choi, Jae Young, Rossi, Ryan, Koh, Eunyee, Lee, Tak Yeon
Format: Conference Proceeding
Language:English
Published: IEEE 22.04.2025
Subjects:
Benchmark testing
Computing methodologies-Artificial intelligence
Data visualization
Design methodology
Faces
Human computer interaction
Human-centered computing-Visualization-Visualization design and evaluation methods
Large language models
Prompt engineering
Reliability
Sensitivity
Visualization
ISSN:2165-8773
Online Access:Get full text
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Summary: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.
ISSN:2165-8773
DOI:10.1109/PacificVis64226.2025.00040