用论文引用模型预测开源AI模型的影响力增长轨迹
Forecasting Open-Weight AI Model Growth on HuggingFace
- 借鉴学术引用规律，用三个参数追踪模型被微调次数
- 多数模型符合预期增长模式，少数突增显示独特影响力
- 适合关注开源生态发展与模型潜力评估的研究者
随着开源权重人工智能模型的快速发展，其研发、投资和用户关注度持续上升，预测哪些模型将推动创新并塑造生态系统变得愈发重要。我们借鉴科学文献中的引用动力学，提出一个量化框架，用于追踪开放权重模型影响力的演变。具体而言，我们采用Wang等人提出的模型，利用三个关键参数——即时性、持久性和相对适应度——来衡量一个开放权重模型被微调的累计次数。研究发现，这种类引用方法能有效捕捉开源模型采纳的多样化轨迹，大多数模型拟合良好，而异常值则揭示了独特的使用模式或使用量的突然跃升。
原文摘要 · Abstract (English)
As the open-weight AI landscape continues to proliferate-with model development, significant investment, and user interest-it becomes increasingly important to predict which models will ultimately drive innovation and shape AI ecosystems. Building on parallels with citation dynamics in scientific literature, we propose a framework to quantify how an open-weight model's influence evolves. Specifically, we adapt the model introduced by Wang et al. for scientific citations, using three key parameters-immediacy, longevity, and relative fitness-to track the cumulative number of fine-tuned models of an open-weight model. Our findings reveal that this citation-style approach can effectively capture the diverse trajectories of open-weight model adoption, with most models fitting well and outliers indicating unique patterns or abrupt jumps in usage.
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