用游戏奖励替代人工标注，收集1万条图像偏好数据。
GameLabel-10K: Collecting Image Preference Data Through Mobile Game Crowdsourcing
- 通过手游玩家打赏机制收集图像偏好数据
- 构建了含近1万标签、7000个提示的GameLabel-10K数据集
- 适合对低成本高质量数据采集感兴趣的开发者
大规模参数模型的发展推动了对数据的强烈需求。本研究探索以游戏玩家替代付费标注员的可行性，通过在移动历史策略游戏Armchair Commander中设置激励机制，让玩家因表现优异获得游戏货币。实验采用成对图像偏好数据，常用于微调扩散模型。基于该方法，我们构建了GameLabel-10K数据集，包含约1万条标注和7000个唯一提示。在该数据集上微调Flux Schnell模型后，其对提示的遵循能力得到提升，验证了该采集方式的有效性。相关数据集与微调模型已公开发布于Hugging Face。
原文摘要 · Abstract (English)
The rise of multi-billion parameter models has sparked an intense hunger for data across deep learning. This study explores the possibility of replacing paid annotators with video game players who are rewarded with in-game currency for good performance. We collaborate with the developers of a mobile historical strategy game, Armchair Commander, to test this idea. More specifically, the current study tests this idea using pairwise image preference data, typically used to fine-tune diffusion models. Using this method, we create GameLabel-10K, a dataset with slightly under 10 thousand labels and 7000 unique prompts. We fine-tune a model on this dataset, we fine-tune Flux Schnell and find an improvement in its prompt adherence, demonstrating the validity of our collection method. In addition, we publicly release both the dataset and our fine-tuned model on Hugging Face.
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