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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