تقرير
Can GPT-4 Models Detect Misleading Visualizations?
| العنوان: | Can GPT-4 Models Detect Misleading Visualizations? |
|---|---|
| المؤلفون: | Alexander, Jason, Nanda, Priyal, Yang, Kai-Cheng, Sarvghad, Ali |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition, Computer Science - Computers and Society, Computer Science - Social and Information Networks |
| الوصف: | The proliferation of misleading visualizations online, particularly during critical events like public health crises and elections, poses a significant risk. This study investigates the capability of GPT-4 models (4V, 4o, and 4o mini) to detect misleading visualizations. Utilizing a dataset of tweet-visualization pairs containing various visual misleaders, we test these models under four experimental conditions with different levels of guidance. We show that GPT-4 models can detect misleading visualizations with moderate accuracy without prior training (naive zero-shot) and that performance notably improves when provided with definitions of misleaders (guided zero-shot). However, a single prompt engineering technique does not yield the best results for all misleader types. Specifically, providing the models with misleader definitions and examples (guided few-shot) proves more effective for reasoning misleaders, while guided zero-shot performs better for design misleaders. This study underscores the feasibility of using large vision-language models to detect visual misinformation and the importance of prompt engineering for optimized detection accuracy. Comment: 5 pages, 2 figures; accepted by IEEE VIS 2024 (https://ieeevis.org/year/2024/program/paper_v-short-1177.html) |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2408.12617 |
| رقم الأكسشن: | edsarx.2408.12617 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |