Chart-to-Experience: Benchmarking Multimodal LLMs for Predicting Experiential Impact of Charts
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908376194088960 |
|---|---|
| author | Kim, Seon Gyeom Choi, Jae Young Rossi, Ryan Koh, Eunyee Lee, Tak Yeon |
| author_facet | Kim, Seon Gyeom Choi, Jae Young Rossi, Ryan Koh, Eunyee Lee, Tak Yeon |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_17374 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Chart-to-Experience: Benchmarking Multimodal LLMs for Predicting Experiential Impact of Charts Kim, Seon Gyeom Choi, Jae Young Rossi, Ryan Koh, Eunyee Lee, Tak Yeon Human-Computer Interaction Computation and Language 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. |
| title | Chart-to-Experience: Benchmarking Multimodal LLMs for Predicting Experiential Impact of Charts |
| topic | Human-Computer Interaction Computation and Language |
| url | https://arxiv.org/abs/2505.17374 |