Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.04221 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913778440863744 |
|---|---|
| author | Nguyen, Rami Huu Maeda, Kenichi Geshvadi, Mahsa Haehn, Daniel |
| author_facet | Nguyen, Rami Huu Maeda, Kenichi Geshvadi, Mahsa Haehn, Daniel |
| contents | Multimodal Large Language Models (MLLMs) have remarkably progressed in analyzing and understanding images. Despite these advancements, accurately regressing values in charts remains an underexplored area for MLLMs. For visualization, how do MLLMs perform when applied to graphical perception tasks? Our paper investigates this question by reproducing Cleveland and McGill's seminal 1984 experiment and comparing it against human task performance. Our study primarily evaluates fine-tuned and pretrained models and zero-shot prompting to determine if they closely match human graphical perception. Our findings highlight that MLLMs outperform human task performance in some cases but not in others. We highlight the results of all experiments to foster an understanding of where MLLMs succeed and fail when applied to data visualization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_04221 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Evaluating Graphical Perception with Multimodal LLMs Nguyen, Rami Huu Maeda, Kenichi Geshvadi, Mahsa Haehn, Daniel Computer Vision and Pattern Recognition Multimodal Large Language Models (MLLMs) have remarkably progressed in analyzing and understanding images. Despite these advancements, accurately regressing values in charts remains an underexplored area for MLLMs. For visualization, how do MLLMs perform when applied to graphical perception tasks? Our paper investigates this question by reproducing Cleveland and McGill's seminal 1984 experiment and comparing it against human task performance. Our study primarily evaluates fine-tuned and pretrained models and zero-shot prompting to determine if they closely match human graphical perception. Our findings highlight that MLLMs outperform human task performance in some cases but not in others. We highlight the results of all experiments to foster an understanding of where MLLMs succeed and fail when applied to data visualization. |
| title | Evaluating Graphical Perception with Multimodal LLMs |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.04221 |