Summary of Assessing the Aesthetic Evaluation Capabilities Of Gpt-4 with Vision: Insights From Group and Individual Assessments, by Yoshia Abe et al.
Assessing the Aesthetic Evaluation Capabilities of GPT-4 with Vision: Insights from Group and Individual Assessments
by Yoshia Abe, Tatsuya Daikoku, Yasuo Kuniyoshi
First submitted to arxiv on: 6 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 A recently developed state-of-the-art language model, GPT-4 with Vision, is evaluated on its ability to perform aesthetic evaluations of images. The model’s performance is compared to human evaluation in two tasks: predicting average group and individual aesthetic ratings. Experimental results show that GPT-4 with Vision outperforms humans in predicting aesthetic evaluations and identifies distinct responses to beauty and ugliness. The study aims to develop an AI system for aesthetic evaluation by integrating traditional deep learning models with large language models, leveraging scientific knowledge of human perception of beauty. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can do many tasks well, but how good are they at judging what’s beautiful or ugly? Researchers tested a new language model called GPT-4 with Vision to see how well it could evaluate images. They compared its performance to humans in two tasks: guessing the average rating given by a group and guessing an individual’s rating. The results showed that this AI model is very good at judging what’s beautiful or ugly, and even noticed different ways people respond to beauty and ugliness. |
Keywords
* Artificial intelligence * Deep learning * Gpt * Language model
