Summary of Gemini Pro Defeated by Gpt-4v: Evidence From Education, By Gyeong-geon Lee et al.
Gemini Pro Defeated by GPT-4V: Evidence from Education
by Gyeong-Geon Lee, Ehsan Latif, Lehong Shi, Xiaoming Zhai
First submitted to arxiv on: 27 Dec 2023
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study compared the classification performance of Gemini Pro and GPT-4V in educational settings, specifically examining their ability to read text-based rubrics and automatically score student-drawn models in science education using visual question answering (VQA) techniques. The researchers employed both quantitative and qualitative analyses on a dataset derived from student-drawn scientific models and NERIF prompting methods. The findings reveal that GPT-4V significantly outperforms Gemini Pro in terms of scoring accuracy and Quadratic Weighted Kappa, with GPT-4V’s superior capability in handling complex multimodal educational tasks making it a more suitable tool for applications involving multimodal data interpretation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looked at how well two AI models, Gemini Pro and GPT-4V, can help teachers score student art projects. The researchers tested these models by giving them text-based instructions and seeing how well they could identify and score the students’ drawings. They found that one model, GPT-4V, is way better than the other at doing this job. This means that GPT-4V is a more useful tool for helping teachers grade student art projects in science class. |
Keywords
» Artificial intelligence » Classification » Gemini » Gpt » Prompting » Question answering
