fraction-ai/GAP

Gamified Adversarial Prompting (GAP): Crowdsourcing AI-weakness-targeting data through gamification. Boost model performance with community-driven, strategic data collection

29
/ 100
Experimental

This project helps AI developers and researchers significantly improve the performance of their visual AI models. By turning data collection into an engaging game, it gathers high-quality data that reveals AI weaknesses. The game presents users with images and challenges them to create questions the AI will answer incorrectly, with points awarded for success. This process generates detailed image-question-answer sets, which are then used to fine-tune and enhance the visual understanding and reasoning capabilities of large multimodal models.

No commits in the last 6 months.

Use this if you are developing or working with visual AI models and need a scalable, effective way to collect high-quality, adversarial data to improve their performance.

Not ideal if you are looking for a pre-trained general-purpose AI model without needing to collect specialized training data.

AI-model-training visual-question-answering data-collection AI-performance-tuning multimodal-AI
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 7 / 25
Maturity 16 / 25
Community 6 / 25

How are scores calculated?

Stars

34

Forks

2

Language

Python

License

Apache-2.0

Last pushed

Oct 10, 2024

Commits (30d)

0

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