Summary of Gamified Crowd-sourcing Of High-quality Data For Visual Fine-tuning, by Shashank Yadav et al.
Gamified crowd-sourcing of high-quality data for visual fine-tuning
by Shashank Yadav, Rohan Tomar, Garvit Jain, Chirag Ahooja, Shubham Chaudhary, Charles Elkan
First submitted to arxiv on: 5 Oct 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 paper introduces Gamified Adversarial Prompting (GAP), a framework that crowdsources high-quality data for visual instruction tuning of large multimodal models. GAP transforms the data collection process into an engaging game, incentivizing players to provide fine-grained, challenging questions and answers that target gaps in the model’s knowledge. The contributions include an approach to capture question-answer pairs from humans that directly address weaknesses in a model’s knowledge, a method for evaluating and rewarding players that successfully incentivizes them to provide high-quality submissions, and a scalable, gamified platform that succeeds in collecting this data from over 50,000 participants in just a few weeks. The implementation of GAP has significantly improved the accuracy of a small multimodal model, namely MiniCPM-Llama3-V-2.5-8B, increasing its GPT score from 0.147 to 0.477 on the dataset, approaching the benchmark set by the much larger GPT-4V. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a game-like platform that helps people contribute high-quality data for training AI models. This data is used to make the models more accurate and knowledgeable. The authors came up with a way to get people to provide good questions and answers that help fix gaps in the model’s understanding. They also developed a system to reward players for providing good data, which motivated many people (over 50,000) to participate. The result is that the AI model became much better at answering questions correctly. |
Keywords
» Artificial intelligence » Gpt » Instruction tuning » Prompting
