Summary of Genai Arena: An Open Evaluation Platform For Generative Models, by Dongfu Jiang et al.
GenAI Arena: An Open Evaluation Platform for Generative Models
by Dongfu Jiang, Max Ku, Tianle Li, Yuansheng Ni, Shizhuo Sun, Rongqi Fan, Wenhu Chen
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 The abstract discusses the limitations of current automatic evaluation metrics for generative AI models, which often fail to capture nuanced quality and user satisfaction. The proposed GenAI-Arena platform addresses this issue by allowing users to actively participate in evaluating generative models through collective feedback and voting. The platform covers three tasks: text-to-image generation, text-to-video generation, and image editing, with 35 open-source generative models evaluated. The abstract highlights the importance of trustworthy evaluation metrics and showcases the GenAI-Arena’s seven-month operation, amassing over 9,000 votes from the community. Additionally, it releases a cleaned version of preference data for the three tasks (GenAI-Bench) to promote research in building model-based evaluation metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure that AI models are good at generating pictures and videos by asking people to vote on how well they do. Right now, we don’t have a way to accurately measure if these models are doing a good job or not. The GenAI-Arena platform lets users decide which generated images and videos are the best. This helps us understand what makes a good model and what doesn’t. |
Keywords
» Artificial intelligence » Image generation
