Papers › Gamified crowd-sourcing of high-quality data for visual fine-tuning
Gamified crowd-sourcing of high-quality data for visual fine-tuning
Shashank Yadav, Rohan Tomar, Garvit Jain, Chirag Ahooja, Shubham Chaudhary, Charles Elkan
This paper introduces Gamified Adversarial Prompting (GAP), a framework that crowd-sources 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. Our contributions include (1) an approach to capture question-answer pairs from humans that directly address weaknesses in a model's knowledge, (2) a method for evaluating and rewarding players that successfully incentivizes them to provide high-quality submissions, and (3) a scalable, gamified platform that succeeds in collecting this data from over 50,000 participants in just a few weeks. Our 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 our dataset, approaching the benchmark set by the much larger GPT-4V. Moreover, we demonstrate that the data generated using MiniCPM-Llama3-V-2.5-8B also enhances its performance across other benchmarks, and exhibits cross-model benefits. Specifically, the same data improves the performance of QWEN2-VL-2B and QWEN2-VL-7B on the same multiple benchmarks.
Code
No code repository is listed for this paper in the archive or in Syntology's graph.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Visual Question Answering | MM-Vet | Qwen2-VL-7B (finetuned on GAP-VQA train) | GPT-4 score | 64.954 | #22 of 231 | Archive leaderboard | report |
| Visual Question Answering | MM-Vet | Qwen2-VL-2B (finetuned on GAP-VQA train) | GPT-4 score | 52.43 | #53 of 231 | Archive leaderboard | report |
| Visual Question Answering | MM-Vet | MiniCPM-Llama3-V-2.5-8B (finetuned on GAP-VQA train) | GPT-4 score | 51.789 | #56 of 231 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections